diff --git a/mshub_res/assets/mindspore/2.5/bit_resnet50.md b/mshub_res/assets/mindspore/2.5/bit_resnet50.md deleted file mode 100644 index 75b2c6129055fe20e925e4cdcbe047ff2296b1ca..0000000000000000000000000000000000000000 --- a/mshub_res/assets/mindspore/2.5/bit_resnet50.md +++ /dev/null @@ -1,132 +0,0 @@ -# bit_resnet50 - ---- - -model-name: bit_resnet50 - -backbone-name: resnet50 - -module-type: cv - -fine-tunable: True - -model-version: 2.5 - -train-dataset: ImageNet2012 - -evaluation: top1acc76.85 | top5acc93.24 - -author: MindSpore team - -update-time: 2025-03-10 - -repo-link: - -user-id: MindSpore - -used-for: inference - -mindspore-version: 2.5 - -asset: - -- file-format: ckpt - asset-link: - asset-sha256: 77dcaf0f - -license: Apache2.0 - -summary: bit_resnet50 is used for cv - ---- - -# BigTransfer - -> [Big Transfer (BiT): General Visual Representation Learning](https://arxiv.org/abs/1912.11370) - -## Introduction - -Transfer of pre-trained representations improves sample efficiency and simplifies hyperparameter tuning when training deep neural networks for vision. -Big Transfer (BiT) can achieve strong performance on more than 20 data sets by combining some carefully selected components and using simple heuristic -methods for transmission. The components distilled by BiT for training models that transfer well are: 1) Big datasets: as the size of the dataset increases, -the optimal performance of the BIT model will also increase. 2) Big architectures: In order to make full use of large datasets, a large enough architecture -is required. 3) Long pre-training time: Pretraining on a larger dataset requires more training epoch and training time. 4) GroupNorm and Weight Standardisation: -BiT use GroupNorm combined with Weight Standardisation instead of BatchNorm. Since BatchNorm performs worse when the number of images on each accelerator is -too low. 5) With BiT fine-tuning, good performance can be achieved even if there are only a few examples of each type on natural images.[[1, 2](#references)] - -## Results - -Our reproduced model performance on ImageNet-1K is reported as follows. - -
- -| Model | Context | Top-1 (%) | Top-5 (%) | Params(M) | Recipe | Download | -| -------------- | -------- | --------- | --------- | --------- | ------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------- | -| bit_resnet50 | D910x8-G | 76.81 | 93.17 | 25.55 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/bit/bit_resnet50_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/bit/BiT_resnet50-1e4795a4.ckpt) | -| bit_resnet50x3 | D910x8-G | 80.63 | 95.12 | 217.31 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/bit/bit_resnet50x3_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/bit/BiT_resnet50x3-a960f91f.ckpt) | -| bit_resnet101 | D910x8-G | 77.93 | 93.75 | 44.54 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/bit/bit_resnet101_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/bit/BiT_resnet101-2efa9106.ckpt) | - -
- -### Notes - -- Context: Training context denoted as {device}x{pieces}-{MS mode}, where mindspore mode can be G - graph mode or F - pynative mode with ms function. For example, D910x8-G is for training on 8 pieces of Ascend 910 NPU using graph mode. -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. - -## Quick Start - -### Preparation - -#### Installation - -Please refer to the [installation instruction](https://github.com/mindspore-lab/mindcv#installation) in MindCV. - -#### Dataset Preparation - -Please download the [ImageNet-1K](https://www.image-net.org/challenges/LSVRC/2012/index.php) dataset for model training and validation. - -### Training - -- Distributed Training - - It is easy to reproduce the reported results with the pre-defined training recipe. For distributed training on multiple Ascend 910 devices, please run - - ```shell - # distributed training on multiple Ascend devices - mpirun -n 8 python train.py --config configs/bit/bit_resnet50_ascend.yaml --data_dir /path/to/imagenet - ``` - - > If the script is executed by the root user, the `--allow-run-as-root` parameter must be added to `mpirun`. - - For detailed illustration of all hyper-parameters, please refer to [config.py](https://github.com/mindspore-lab/mindcv/blob/main/config.py). - - **Note:** As the global batch size (batch_size x num_devices) is an important hyper-parameter, it is recommended to keep the global batch size unchanged for reproduction or adjust the learning rate linearly to a new global batch size. - -- Standalone Training - - If you want to train or finetune the model on a smaller dataset without distributed training, please run: - - ```shell - # standalone training on a CPU/Ascend device - python train.py --config configs/bit/bit_resnet50_ascend.yaml --data_dir /path/to/dataset --distribute False - ``` - -### Validation - -To validate the accuracy of the trained model, you can use `validate.py` and parse the checkpoint path with `--ckpt_path`. - -```shell -python validate.py -c configs/bit/bit_resnet50_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt -``` - -### Deployment - -Please refer to the [deployment tutorial](https://mindspore-lab.github.io/mindcv/zh/tutorials/inference/) in MindCV. - -## References - - - -[1] Kolesnikov A, Beyer L, Zhai X, et al. Big transfer (bit): General visual representation learning[C]//European conference on computer vision. Springer, Cham, 2020: 491-507. - -[2] BigTransfer (BiT): State-of-the-art transfer learning for computer vision, diff --git a/mshub_res/assets/mindspore/2.5/bit_resnet50_imagenet2012.md b/mshub_res/assets/mindspore/2.5/bit_resnet50_imagenet2012.md index d542ba7f204f5249b0d14f0dc85d4e4026f99fbe..2036707fcfbed14ee5205e793bbf8836352c1d1f 100644 --- a/mshub_res/assets/mindspore/2.5/bit_resnet50_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/bit_resnet50_imagenet2012.md @@ -68,12 +68,6 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. _coming soon_ -Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode. - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ------------ | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------- | -| bit_resnet50 | 25.55 | 8 | 32 | 224x224 | O2 | 146s | 74.52 | 3413.33 | 76.81 | 93.17 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/bit/bit_resnet50_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/bit/BiT_resnet50-1e4795a4.ckpt) | - ### Notes - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. diff --git a/mshub_res/assets/mindspore/2.5/coat_lite_tiny.md b/mshub_res/assets/mindspore/2.5/coat_lite_tiny.md deleted file mode 100644 index 9c83390e7f2da168099ae0c270949d4ce2360ce4..0000000000000000000000000000000000000000 --- a/mshub_res/assets/mindspore/2.5/coat_lite_tiny.md +++ /dev/null @@ -1,123 +0,0 @@ -# coat_tiny - ---- - -model-name: coat_tiny - -backbone-name: coat - -module-type: cv - -fine-tunable: True - -model-version: 2.5 - -train-dataset: ImageNet2012 - -evaluation: top1acc79.28 | top5acc94.45 - -author: MindSpore team - -update-time: 2025-03-10 - -repo-link: - -user-id: MindSpore - -used-for: inference - -mindspore-version: 2.5 - -asset: - -- file-format: ckpt - asset-link: - asset-sha256: dcca16b1 - -license: Apache2.0 - -summary: coat is used for cv - ---- - -# CoaT - -> [Co-Scale Conv-Attentional Image Transformers](https://arxiv.org/abs/2104.06399v2) - -## Introduction - -Co-Scale Conv-Attentional Image Transformer (CoaT) is a Transformer-based image classifier equipped with co-scale and conv-attentional mechanisms. First, the co-scale mechanism maintains the integrity of Transformers' encoder branches at individual scales, while allowing representations learned at different scales to effectively communicate with each other. Second, the conv-attentional mechanism is designed by realizing a relative position embedding formulation in the factorized attention module with an efficient convolution-like implementation. CoaT empowers image Transformers with enriched multi-scale and contextual modeling capabilities. - -## Results - -Our reproduced model performance on ImageNet-1K is reported as follows. - -
- -| Model | Context | Top-1 (%) | Top-5 (%) | Params (M) | Recipe | Weight | -| -------------- | -------- | --------- | --------- | ---------- | ------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------- | -| coat_lite_tiny | D910x8-G | 77.35 | 93.43 | 5.72 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/coat/coat_lite_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/coat/coat_lite_tiny-fa7bf894.ckpt) | -| coat_lite_mini | D910x8-G | 78.51 | 93.84 | 11.01 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/coat/coat_lite_mini_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/coat/coat_lite_mini-55a52f05.ckpt) | -| coat_tiny | D910x8-G | 79.67 | 94.88 | 5.50 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/coat/coat_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/coat/coat_tiny-071cb792.ckpt) | -| coat_mini | D910x8-G | 81.08 | 95.34 | 10.34 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/coat/coat_mini_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/coat/coat_mini-57c5bce7.ckpt) | - -
- -### Notes - -- Context: Training context denoted as {device}x{pieces}-{MS mode}, where mindspore mode can be G - graph mode or F - pynative mode with ms function. For example, D910x8-G is for training on 8 pieces of Ascend 910 NPU using graph mode. -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. - -## Quick Start - -### Preparation - -#### Installation - -Please refer to the [installation instruction](https://github.com/mindspore-lab/mindcv#installation) in MindCV. - -#### Dataset Preparation - -Please download the [ImageNet-1K](https://www.image-net.org/challenges/LSVRC/2012/index.php) dataset for model training and validation. - -### Training - -- Distributed Training - - It is easy to reproduce the reported results with the pre-defined training recipe. For distributed training on multiple Ascend 910 devices, please run - - ```shell - # distributed training on multiple Ascend devices - mpirun -n 8 python train.py --config configs/coat/coat_lite_tiny_ascend.yaml --data_dir /path/to/imagenet - ``` - - > If the script is executed by the root user, the `--allow-run-as-root` parameter must be added to `mpirun` - - For detailed illustration of all hyper-parameters, please refer to [config.py](https://github.com/mindspore-lab/mindcv/blob/main/config.py). - - **Note:** As the global batch size (batch_size x num_devices) is an important hyper-parameter, it is recommended to keep the global batch size unchanged for reproduction or adjust the learning rate linearly to a new global batch size. - -- Standalone Training - - If you want to train or finetune the model on a smaller dataset without distributed training, please run: - - ```shell - # standalone training on a CPU/Ascend device - python train.py --config configs/coat/coat_lite_tiny_ascend.yaml --data_dir /path/to/dataset --distribute False - ``` - -### Validation - -To validate the accuracy of the trained model, you can use `validate.py` and parse the checkpoint path with `--ckpt_path`. - -```shell -python validate.py -c configs/coat/coat_lite_tiny_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt -``` - -### Deployment - -To deploy online inference services with the trained model efficiently, please refer to the [deployment tutorial](https://mindspore-lab.github.io/mindcv/zh/tutorials/inference/). - -## References - -[1] Han D, Yun S, Heo B, et al. Rethinking channel dimensions for efficient model design[C]//Proceedings of the IEEE/CVF conference on Computer Vision and Pattern Recognition. 2021: 732-741. diff --git a/mshub_res/assets/mindspore/2.5/coat_tiny_imagenet2012.md b/mshub_res/assets/mindspore/2.5/coat_tiny_imagenet2012.md index fb5e7998982ae13f18a942842a72ae0aa8541aab..c639d4277fcd8f79ac93f99ca92e39a30b94f7ba 100644 --- a/mshub_res/assets/mindspore/2.5/coat_tiny_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/coat_tiny_imagenet2012.md @@ -62,12 +62,6 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. _coming soon_ -Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode. - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | -------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------- | -| coat_tiny | 5.50 | 8 | 32 | 224x224 | O2 | 543s | 254.95 | 1003.92 | 79.67 | 94.88 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/coat/coat_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/coat/coat_tiny-071cb792.ckpt) | - ### Notes - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. diff --git a/mshub_res/assets/mindspore/2.5/convit.md b/mshub_res/assets/mindspore/2.5/convit.md deleted file mode 100644 index 5d2bf6c4b3ae860577eeef159489b91c7c48522c..0000000000000000000000000000000000000000 --- a/mshub_res/assets/mindspore/2.5/convit.md +++ /dev/null @@ -1,141 +0,0 @@ -# convit_tiny - ---- - -model-name: convit_tiny - -backbone-name: convit - -module-type: cv - -fine-tunable: True - -model-version: 2.5 - -train-dataset: ImageNet2012 - -evaluation: top1acc73.79 | top5acc91.70 - -author: MindSpore team - -update-time: 2025-03-10 - -repo-link: - -user-id: MindSpore - -used-for: inference - -mindspore-version: 2.5 - -asset: - -- file-format: ckpt - asset-link: - asset-sha256: 1961717e - -license: Apache2.0 - -summary: convit is used for cv - ---- - -# ConViT - -> [ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases](https://arxiv.org/abs/2103.10697) - -## Introduction - -ConViT combines the strengths of convolutional architectures and Vision Transformers (ViTs). -ConViT introduces gated positional self-attention (GPSA), a form of positional self-attention -that can be equipped with a “soft” convolutional inductive bias. -ConViT initializes the GPSA layers to mimic the locality of convolutional layers, -then gives each attention head the freedom to escape locality by adjusting a gating parameter -regulating the attention paid to position versus content information. -ConViT, outperforms the DeiT (Touvron et al., 2020) on ImageNet, -while offering a much improved sample efficiency.[[1](#references)] - -

- -

-

- Figure 1. Architecture of ConViT [1] -

- -## Results - -Our reproduced model performance on ImageNet-1K is reported as follows. - -
- -| Model | Context | Top-1 (%) | Top-5 (%) | Params (M) | Recipe | Download | -| ----------------- | -------- | --------- | --------- | ---------- | ------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------- | -| convit_tiny | D910x8-G | 73.66 | 91.72 | 5.71 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convit/convit_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/convit/convit_tiny-e31023f2.ckpt) | -| convit_tiny_plus | D910x8-G | 77.00 | 93.60 | 9.97 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convit/convit_tiny_plus_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/convit/convit_tiny_plus-e9d7fb92.ckpt) | -| convit_small | D910x8-G | 81.63 | 95.59 | 27.78 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convit/convit_small_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/convit/convit_small-ba858604.ckpt) | -| convit_small_plus | D910x8-G | 81.80 | 95.42 | 48.98 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convit/convit_small_plus_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/convit/convit_small_plus-2352b9f7.ckpt) | -| convit_base | D910x8-G | 82.10 | 95.52 | 86.54 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convit/convit_base_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/convit/convit_base-c61b808c.ckpt) | -| convit_base_plus | D910x8-G | 81.96 | 95.04 | 153.13 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convit/convit_base_plus_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/convit/convit_base_plus-5c61c9ce.ckpt) | - -
- -### Notes - -- Context: Training context denoted as {device}x{pieces}-{MS mode}, where mindspore mode can be G - graph mode or F - pynative mode with ms function. For example, D910x8-G is for training on 8 pieces of Ascend 910 NPU using graph mode. -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. - -## Quick Start - -### Preparation - -#### Installation - -Please refer to the [installation instruction](https://github.com/mindspore-ecosystem/mindcv#installation) in MindCV. - -#### Dataset Preparation - -Please download the [ImageNet-1K](https://www.image-net.org/challenges/LSVRC/2012/index.php) dataset for model training and validation. - -### Training - -- Distributed Training - - It is easy to reproduce the reported results with the pre-defined training recipe. For distributed training on multiple Ascend 910 devices, please run - - ```shell - # distributed training on multiple Ascend devices - mpirun -n 8 python train.py --config configs/convit/convit_tiny_ascend.yaml --data_dir /path/to/imagenet - ``` - - > If the script is executed by the root user, the `--allow-run-as-root` parameter must be added to `mpirun`. - - For detailed illustration of all hyper-parameters, please refer to [config.py](https://github.com/mindspore-lab/mindcv/blob/main/config.py). - - **Note:** As the global batch size (batch_size x num_devices) is an important hyper-parameter, it is recommended to keep the global batch size unchanged for reproduction or adjust the learning rate linearly to a new global batch size. - -- Standalone Training - - If you want to train or finetune the model on a smaller dataset without distributed training, please run: - - ```shell - # standalone training on a CPU/Ascend device - python train.py --config configs/convit/convit_tiny_ascend.yaml --data_dir /path/to/dataset --distribute False - ``` - -### Validation - -To validate the accuracy of the trained model, you can use `validate.py` and parse the checkpoint path with `--ckpt_path`. - -```shell -python validate.py -c configs/convit/convit_tiny_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt -``` - -### Deployment - -Please refer to the [deployment tutorial](https://mindspore-lab.github.io/mindcv/zh/tutorials/inference/) in MindCV. - -## References - - - -[1] d’Ascoli S, Touvron H, Leavitt M L, et al. Convit: Improving vision transformers with soft convolutional inductive biases[C]//International Conference on Machine Learning. PMLR, 2021: 2286-2296. diff --git a/mshub_res/assets/mindspore/2.5/convit_tiny_imagenet2012.md b/mshub_res/assets/mindspore/2.5/convit_tiny_imagenet2012.md index a814af4891b90688b8749dac20888100281c394b..dc49ac87189eaabdf959d69f700a24c03dbd0027 100644 --- a/mshub_res/assets/mindspore/2.5/convit_tiny_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/convit_tiny_imagenet2012.md @@ -78,12 +78,6 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. | ----------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------- | | convit_tiny | 5.71 | 8 | 256 | 224x224 | O2 | 153s | 226.51 | 9022.03 | 73.79 | 91.70 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convit/convit_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/convit/convit_tiny-1961717e-910v2.ckpt) | -Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode. - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ----------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------- | -| convit_tiny | 5.71 | 8 | 256 | 224x224 | O2 | 133s | 231.62 | 8827.59 | 73.66 | 91.72 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convit/convit_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/convit/convit_tiny-e31023f2.ckpt) | - ### Notes - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. diff --git a/mshub_res/assets/mindspore/2.5/convnext.md b/mshub_res/assets/mindspore/2.5/convnext.md deleted file mode 100644 index 6bd1efa82fd6be150086b30bc7402be4a2d63719..0000000000000000000000000000000000000000 --- a/mshub_res/assets/mindspore/2.5/convnext.md +++ /dev/null @@ -1,134 +0,0 @@ -# convnext_tiny - ---- - -model-name: convnext_tiny - -backbone-name: convnext - -module-type: cv - -fine-tunable: True - -model-version: 2.5 - -train-dataset: ImageNet2012 - -evaluation: top1acc81.28 | top5acc95.61 - -author: MindSpore team - -update-time: 2025-03-10 - -repo-link: - -user-id: MindSpore - -used-for: inference - -mindspore-version: 2.5 - -asset: - -- file-format: ckpt - asset-link: - asset-sha256: db11dc82 - -license: Apache2.0 - -summary: convnext is used for cv - ---- - -# convnext_tiny - -> [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) - -## Introduction - -In this work, the authors reexamine the design spaces and test the limits of what a pure ConvNet can achieve. -The authors gradually "modernize" a standard ResNet toward the design of a vision Transformer, and discover several key -components that contribute to the performance difference along the way. The outcome of this exploration is a family of -pure ConvNet models dubbed ConvNeXt. Constructed entirely from standard ConvNet modules, ConvNeXts compete favorably -with Transformers in terms of accuracy and scalability, achieving 87.8% ImageNet top-1 accuracy, while maintaining the -simplicity and efficiency of standard ConvNets.[[1](#references)] - -

- -

-

- Figure 1. Architecture of ConvNeXt [1] -

- -## Results - -Our reproduced model performance on ImageNet-1K is reported as follows. - -
- -| Model | Context | Top-1 (%) | Top-5 (%) | Params (M) | Recipe | Download | -| -------------- | --------- | --------- | --------- | ---------- | ----------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------ | -| convnext_tiny | D910x64-G | 81.91 | 95.79 | 28.59 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnext/convnext_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/convnext/convnext_tiny-ae5ff8d7.ckpt) | -| convnext_small | D910x64-G | 83.40 | 96.36 | 50.22 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnext/convnext_small_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/convnext/convnext_small-e23008f3.ckpt) | -| convnext_base | D910x64-G | 83.32 | 96.24 | 88.59 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnext/convnext_base_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/convnext/convnext_base-ee3544b8.ckpt) | - -
- -### Notes - -- Context: Training context denoted as {device}x{pieces}-{MS mode}, where mindspore mode can be G - graph mode or F - pynative mode with ms function. For example, D910x8-G is for training on 8 pieces of Ascend 910 NPU using graph mode. -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. - -## Quick Start - -### Preparation - -#### Installation - -Please refer to the [installation instruction](https://github.com/mindspore-ecosystem/mindcv#installation) in MindCV. - -#### Dataset Preparation - -Please download the [ImageNet-1K](https://www.image-net.org/challenges/LSVRC/2012/index.php) dataset for model training and validation. - -### Training - -- Distributed Training - - It is easy to reproduce the reported results with the pre-defined training recipe. For distributed training on multiple Ascend 910 devices, please run - - ```shell - # distributed training on multiple Ascend devices - mpirun -n 8 python train.py --config configs/convnext/convnext_tiny_ascend.yaml --data_dir /path/to/imagenet - ``` - - > If the script is executed by the root user, the `--allow-run-as-root` parameter must be added to `mpirun`. - - For detailed illustration of all hyper-parameters, please refer to [config.py](https://github.com/mindspore-lab/mindcv/blob/main/config.py). - - **Note:** As the global batch size (batch_size x num_devices) is an important hyper-parameter, it is recommended to keep the global batch size unchanged for reproduction or adjust the learning rate linearly to a new global batch size. - -- Standalone Training - - If you want to train or finetune the model on a smaller dataset without distributed training, please run: - - ```shell - # standalone training on a CPU/Ascend device - python train.py --config configs/convnext/convnext_tiny_ascend.yaml --data_dir /path/to/dataset --distribute False - ``` - -### Validation - -To validate the accuracy of the trained model, you can use `validate.py` and parse the checkpoint path with `--ckpt_path`. - -```shell -python validate.py -c configs/convnext/convnext_tiny_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt -``` - -### Deployment - -Please refer to the [deployment tutorial](https://mindspore-lab.github.io/mindcv/zh/tutorials/inference/) in MindCV. - -## References - -[1] Liu Z, Mao H, Wu C Y, et al. A convnet for the 2020s[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022: 11976-11986. diff --git a/mshub_res/assets/mindspore/2.5/convnext_tiny_imagenet2012.md b/mshub_res/assets/mindspore/2.5/convnext_tiny_imagenet2012.md index dd69d4ac91dea60db1410c7e7c859157098b23d0..5fb468a2ae4834de1289b859174c315663106788 100644 --- a/mshub_res/assets/mindspore/2.5/convnext_tiny_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/convnext_tiny_imagenet2012.md @@ -76,12 +76,6 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. | ------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------- | | convnext_tiny | 28.59 | 8 | 16 | 224x224 | O2 | 137s | 48.7 | 2612.24 | 81.28 | 95.61 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnext/convnext_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/convnext/convnext_tiny-db11dc82-910v2.ckpt) | -Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode. - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------- | -| convnext_tiny | 28.59 | 8 | 16 | 224x224 | O2 | 127s | 66.79 | 1910.45 | 81.91 | 95.79 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnext/convnext_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/convnext/convnext_tiny-ae5ff8d7.ckpt) | - ### Notes - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. diff --git a/mshub_res/assets/mindspore/2.5/convnextv2.md b/mshub_res/assets/mindspore/2.5/convnextv2.md deleted file mode 100644 index 382fc53b06029d831683287715b414d16c903df6..0000000000000000000000000000000000000000 --- a/mshub_res/assets/mindspore/2.5/convnextv2.md +++ /dev/null @@ -1,131 +0,0 @@ -# convnextv2_tiny - ---- - -model-name: convnextv2_tiny - -backbone-name: convnextv2 - -module-type: cv - -fine-tunable: True - -model-version: 2.5 - -train-dataset: ImageNet2012 - -evaluation: top1acc82.39 | top5acc95.95 - -author: MindSpore team - -update-time: 2025-03-10 - -repo-link: - -user-id: MindSpore - -used-for: inference - -mindspore-version: 2.35 - -asset: - -- file-format: ckpt - asset-link: - asset-sha256: a35b79ce - -license: Apache2.0 - -summary: convnextv2 is used for cv - ---- - -# convnextv2_tiny - -> [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://arxiv.org/abs/2301.00808) - -## Introduction - -In this paper, the authors propose a fully convolutional masked autoencoder framework and a new Global Response -Normalization (GRN) layer that can be added to the ConvNeXt architecture to enhance inter-channel feature competition. -This co-design of self-supervised learning techniques (such as MAE) and architectural improvement results in a new model -family called ConvNeXt V2, which significantly improves the performance of pure ConvNets on various recognition -benchmarks, including ImageNet classification, COCO detection, and ADE20K segmentation.[[1](#references)] - -

- -

-

- Figure 1. Architecture of ConvNeXt V2 [1] -

- -## Results - -Our reproduced model performance on ImageNet-1K is reported as follows. - -
- -| Model | Context | Top-1 (%) | Top-5 (%) | Params (M) | Recipe | Download | -| --------------- | -------- | --------- | --------- | ---------- | -------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------- | -| convnextv2_tiny | D910x8-G | 82.43 | 95.98 | 28.64 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnextv2/convnextv2_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/convnextv2/convnextv2_tiny-d441ba2c.ckpt) | - -
- -### Notes - -- Context: Training context denoted as {device}x{pieces}-{MS mode}, where mindspore mode can be G - graph mode or F - pynative mode with ms function. For example, D910x8-G is for training on 8 pieces of Ascend 910 NPU using graph mode. -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. - -## Quick Start - -### Preparation - -#### Installation - -Please refer to the [installation instruction](https://github.com/mindspore-ecosystem/mindcv#installation) in MindCV. - -#### Dataset Preparation - -Please download the [ImageNet-1K](https://www.image-net.org/challenges/LSVRC/2012/index.php) dataset for model training and validation. - -### Training - -- Distributed Training - - It is easy to reproduce the reported results with the pre-defined training recipe. For distributed training on multiple Ascend 910 devices, please run - - ```shell - # distributed training on multiple Ascend devices - mpirun -n 8 python train.py --config configs/convnextv2/convnextv2_tiny_ascend.yaml --data_dir /path/to/imagenet - ``` - - > If the script is executed by the root user, the `--allow-run-as-root` parameter must be added to `mpirun`. - - For detailed illustration of all hyper-parameters, please refer to [config.py](https://github.com/mindspore-lab/mindcv/blob/main/config.py). - - **Note:** As the global batch size (batch_size x num_devices) is an important hyper-parameter, it is recommended to keep the global batch size unchanged for reproduction or adjust the learning rate linearly to a new global batch size. - -- Standalone Training - - If you want to train or finetune the model on a smaller dataset without distributed training, please run: - - ```shell - # standalone training on a CPU/Ascend device - python train.py --config configs/convnextv2/convnextv2_tiny_ascend.yaml --data_dir /path/to/dataset --distribute False - ``` - -### Validation - -To validate the accuracy of the trained model, you can use `validate.py` and parse the checkpoint path with `--ckpt_path`. - -```shell -python validate.py -c configs/convnextv2/convnextv2_tiny_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt -``` - -### Deployment - -Please refer to the [deployment tutorial](https://mindspore-lab.github.io/mindcv/zh/tutorials/inference/) in MindCV. - -## References - -[1] Woo S, Debnath S, Hu R, et al. ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders[J]. arXiv preprint arXiv:2301.00808, 2023. diff --git a/mshub_res/assets/mindspore/2.5/convnextv2_tiny_imagenet2012.md b/mshub_res/assets/mindspore/2.5/convnextv2_tiny_imagenet2012.md index e7df0555020cb37fffca0b738937b4b80c324d36..01bf619eb9bdef80d1840843f5ac704fe49b972b 100644 --- a/mshub_res/assets/mindspore/2.5/convnextv2_tiny_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/convnextv2_tiny_imagenet2012.md @@ -75,12 +75,6 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. | --------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | -------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------- | | convnextv2_tiny | 28.64 | 8 | 128 | 224x224 | O2 | 268s | 257.2 | 3984.44 | 82.39 | 95.95 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnextv2/convnextv2_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/convnextv2/convnextv2_tiny-a35b79ce-910v2.ckpt) | -Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode. - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| --------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | -------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------- | -| convnextv2_tiny | 28.64 | 8 | 128 | 224x224 | O2 | 237s | 400.20 | 2560.00 | 82.43 | 95.98 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnextv2/convnextv2_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/convnextv2/convnextv2_tiny-d441ba2c.ckpt) | - ### Notes - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. diff --git a/mshub_res/assets/mindspore/2.5/crnn_vgg7_lmdbd.md b/mshub_res/assets/mindspore/2.5/crnn_vgg7_lmdbd.md index b726c7af7eac42a487547776a18cc9c880bdc323..ba20f04b20c395573150937470d00f34c9281835 100644 --- a/mshub_res/assets/mindspore/2.5/crnn_vgg7_lmdbd.md +++ b/mshub_res/assets/mindspore/2.5/crnn_vgg7_lmdbd.md @@ -132,11 +132,11 @@ Here we used the datasets under `training/` folders for **training**, and the un **Training:** (total 14,442,049 samples) - [MJSynth (MJ)](http://www.robots.ox.ac.uk/~vgg/data/text/) -- Train: 21.2 GB, 7224586 samples -- Valid: 2.36 GB, 802731 samples -- Test: 2.61 GB, 891924 samples + - Train: 21.2 GB, 7224586 samples + - Valid: 2.36 GB, 802731 samples + - Test: 2.61 GB, 891924 samples - [SynthText (ST)](https://academictorrents.com/details/2dba9518166cbd141534cbf381aa3e99a087e83c) -- Train: 16.0 GB, 5522808 samples + - Train: 16.0 GB, 5522808 samples **Validation:** diff --git a/mshub_res/assets/mindspore/2.5/crossvit.md b/mshub_res/assets/mindspore/2.5/crossvit.md deleted file mode 100644 index dff93c16c5fca38884fb81aff4cf985ed275fe3b..0000000000000000000000000000000000000000 --- a/mshub_res/assets/mindspore/2.5/crossvit.md +++ /dev/null @@ -1,133 +0,0 @@ -# crossvit_9 - ---- - -model-name: crossvit_9 - -backbone-name: crossvit - -module-type: cv - -fine-tunable: True - -model-version: 2.5 - -train-dataset: ImageNet2012 - -evaluation: top1acc73.38 | top5acc91.51 - -author: MindSpore team - -update-time: 2025-03-10 - -repo-link: - -user-id: MindSpore - -used-for: inference - -mindspore-version: 2.5 - -asset: - -- file-format: ckpt - asset-link: - asset-sha256: 32c69c96 - -license: Apache2.0 - -summary: crossvit is used for cv - ---- - -# CrossViT - -> [CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification](https://arxiv.org/abs/2103.14899) - -## Introduction - -CrossViT is a type of vision transformer that uses a dual-branch architecture to extract multi-scale feature representations for image classification. The architecture combines image patches (i.e. tokens in a transformer) of different sizes to produce stronger visual features for image classification. It processes small and large patch tokens with two separate branches of different computational complexities and these tokens are fused together multiple times to complement each other. - -Fusion is achieved by an efficient cross-attention module, in which each transformer branch creates a non-patch token as an agent to exchange information with the other branch by attention. This allows for linear-time generation of the attention map in fusion instead of quadratic time otherwise.[[1](#references)] - -

- -

-

- Figure 1. Architecture of CrossViT [1] -

- -## Results - -Our reproduced model performance on ImageNet-1K is reported as follows. - -
- -| Model | Context | Top-1 (%) | Top-5 (%) | Params (M) | Recipe | Download | -| ----------- | -------- | --------- | --------- | ---------- | -------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------- | -| crossvit_9 | D910x8-G | 73.56 | 91.79 | 8.55 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/crossvit/crossvit_9_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/crossvit/crossvit_9-e74c8e18.ckpt) | -| crossvit_15 | D910x8-G | 81.08 | 95.33 | 27.27 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/crossvit/crossvit_15_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/crossvit/crossvit_15-eaa43c02.ckpt) | -| crossvit_18 | D910x8-G | 81.93 | 95.75 | 43.27 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/crossvit/crossvit_18_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/crossvit/crossvit_18-ca0a2e43.ckpt) | - -
- -### Notes - -- Context: Training context denoted as {device}x{pieces}-{MS mode}, where mindspore mode can be G - graph mode or F - pynative mode with ms function. For example, D910x8-G is for training on 8 pieces of Ascend 910 NPU using graph mode. -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. - -## Quick Start - -### Preparation - -#### Installation - -Please refer to the [installation instruction](https://github.com/mindspore-ecosystem/mindcv#installation) in MindCV. - -#### Dataset Preparation - -Please download the [ImageNet-1K](https://www.image-net.org/challenges/LSVRC/2012/index.php) dataset for model training and validation. - -### Training - -- Distributed Training - - It is easy to reproduce the reported results with the pre-defined training recipe. For distributed training on multiple Ascend 910 devices, please run - - ```shell - # distributed training on multiple Ascend devices - mpirun -n 8 python train.py --config configs/crossvit/crossvit_15_ascend.yaml --data_dir /path/to/imagenet - ``` - - > If the script is executed by the root user, the `--allow-run-as-root` parameter must be added to `mpirun`. - - For detailed illustration of all hyper-parameters, please refer to [config.py](https://github.com/mindspore-lab/mindcv/blob/main/config.py). - - **Note:** As the global batch size (batch_size x num_devices) is an important hyper-parameter, it is recommended to keep the global batch size unchanged for reproduction or adjust the learning rate linearly to a new global batch size. - -- Standalone Training - - If you want to train or finetune the model on a smaller dataset without distributed training, please run: - - ```shell - # standalone training on a CPU/Ascend device - python train.py --config configs/crossvit/crossvit_15_ascend.yaml --data_dir /path/to/dataset --distribute False - ``` - -### Validation - -To validate the accuracy of the trained model, you can use `validate.py` and parse the checkpoint path with `--ckpt_path`. - -```shell -python validate.py -c configs/crossvit/crossvit_15_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt -``` - -### Deployment - -Please refer to the [deployment tutorial](https://mindspore-lab.github.io/mindcv/zh/tutorials/inference/) in MindCV. - -## References - - - -[1] Chun-Fu Chen, Quanfu Fan, Rameswar Panda. CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification diff --git a/mshub_res/assets/mindspore/2.5/crossvit_9_imagenet2012.md b/mshub_res/assets/mindspore/2.5/crossvit_9_imagenet2012.md index 9ab454c4ee9a0e077a7d283fd3067fd5b9ca2f88..cdf7f5c12cbcb1c1e83d5fb9b17816a83c803030 100644 --- a/mshub_res/assets/mindspore/2.5/crossvit_9_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/crossvit_9_imagenet2012.md @@ -73,12 +73,6 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. | ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------- | | crossvit_9 | 8.55 | 8 | 256 | 240x240 | O2 | 221s | 514.36 | 3984.44 | 73.38 | 91.51 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/crossvit/crossvit_9_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/crossvit/crossvit_9-32c69c96-910v2.ckpt) | -Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode. - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------ | -| crossvit_9 | 8.55 | 8 | 256 | 240x240 | O2 | 206s | 550.79 | 3719.30 | 73.56 | 91.79 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/crossvit/crossvit_9_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/crossvit/crossvit_9-e74c8e18.ckpt) | - ### Notes - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. diff --git a/mshub_res/assets/mindspore/2.5/dbnet_mobilenetv3_icdar2015.md b/mshub_res/assets/mindspore/2.5/dbnet_mobilenetv3_icdar2015.md index 474d116efab3a86d8569f98a921c4bbb42de1bd8..4bdf50b691e9fc6aecbfb4bf4e5740ce2ad0fe89 100644 --- a/mshub_res/assets/mindspore/2.5/dbnet_mobilenetv3_icdar2015.md +++ b/mshub_res/assets/mindspore/2.5/dbnet_mobilenetv3_icdar2015.md @@ -45,6 +45,7 @@ summary: dbnet_mobilenetv3 is used for cv > DBNet: [Real-time Scene Text Detection with Differentiable Binarization](https://arxiv.org/abs/1911.08947) +> > DBNet++: [Real-Time Scene Text Detection with Differentiable Binarization and Adaptive Scale Fusion](https://arxiv.org/abs/2202.10304) ## Introduction diff --git a/mshub_res/assets/mindspore/2.5/densenet121_imagenet2012.md b/mshub_res/assets/mindspore/2.5/densenet121_imagenet2012.md index 8746f0886ab5c741ec2ed97d1cd27749ac646609..e95eccefe833beb63d6286cbe6868aa5c8b05fb2 100644 --- a/mshub_res/assets/mindspore/2.5/densenet121_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/densenet121_imagenet2012.md @@ -6,7 +6,7 @@ model-name: densenet121 backbone-name: densenet -module-type: cv-classification +module-type: cv fine-tunable: True @@ -83,12 +83,6 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. | ----------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------- | | densenet121 | 8.06 | 8 | 32 | 224x224 | O2 | 300s | 47,34 | 5446.81 | 75.67 | 92.77 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/densenet/densenet_121_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/densenet/densenet121-bf4ab27f-910v2.ckpt) | -Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode. - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ----------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------- | -| densenet121 | 8.06 | 8 | 32 | 224x224 | O2 | 191s | 43.28 | 5914.97 | 75.64 | 92.84 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/densenet/densenet_121_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/densenet/densenet121-120_5004_Ascend.ckpt) | - ### Notes - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. diff --git a/mshub_res/assets/mindspore/2.5/edgenext.md b/mshub_res/assets/mindspore/2.5/edgenext.md deleted file mode 100644 index 52cfeaa2e64d623b072658e97574065fbc0189e6..0000000000000000000000000000000000000000 --- a/mshub_res/assets/mindspore/2.5/edgenext.md +++ /dev/null @@ -1,136 +0,0 @@ -# edgenext_xx_small - ---- - -model-name: edgenext_xx_small - -backbone-name: edgenext - -module-type: cv - -fine-tunable: True - -model-version: 2.5 - -train-dataset: ImageNet2012 - -evaluation: top1acc70.64 | top5acc89.75 - -author: MindSpore team - -update-time: 2025-03-10 - -repo-link: - -user-id: MindSpore - -used-for: inference - -mindspore-version: 2.5 - -asset: - -- file-format: ckpt - asset-link: - asset-sha256: cad13d2c - -license: Apache2.0 - -summary: edgenext is used for cv - ---- - -# edgenext_xx_small - -> [EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications](https://arxiv.org/abs/2206.10589) - -## Introduction - -EdgeNeXt effectively combines the strengths of both CNN and Transformer models and is a -new efficient hybrid architecture. EdgeNeXt introduces a split depth-wise transpose -attention (SDTA) encoder that splits input tensors into multiple channel groups and -utilizes depth-wise convolution along with self-attention across channel dimensions -to implicitly increase the receptive field and encode multi-scale features.[[1](#references)] - -

- -

-

- Figure 1. Architecture of EdgeNeXt [1] -

- -## Results - -Our reproduced model performance on ImageNet-1K is reported as follows. - -
- -| Model | Context | Top-1 (%) | Top-5 (%) | Params (M) | Recipe | Download | -| ----------------- | -------- | --------- | --------- | ---------- | -------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------- | -| edgenext_xx_small | D910x8-G | 71.02 | 89.99 | 1.33 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/edgenext/edgenext_xx_small_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/edgenext/edgenext_xx_small-afc971fb.ckpt) | -| edgenext_x_small | D910x8-G | 75.14 | 92.50 | 2.34 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/edgenext/edgenext_x_small_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/edgenext/edgenext_x_small-a200c6fc.ckpt) | -| edgenext_small | D910x8-G | 79.15 | 94.39 | 5.59 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/edgenext/edgenext_small_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/edgenext/edgenext_small-f530c372.ckpt) | -| edgenext_base | D910x8-G | 82.24 | 95.94 | 18.51 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/edgenext/edgenext_base_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/edgenext/edgenext_base-4335e9dc.ckpt) | - -
- -### Notes - -- Context: Training context denoted as {device}x{pieces}-{MS mode}, where mindspore mode can be G - graph mode or F - pynative mode with ms function. For example, D910x8-G is for training on 8 pieces of Ascend 910 NPU using graph mode. -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. - -## Quick Start - -### Preparation - -#### Installation - -Please refer to the [installation instruction](https://github.com/mindspore-lab/mindcv#installation) in MindCV. - -#### Dataset Preparation - -Please download the [ImageNet-1K](https://www.image-net.org/challenges/LSVRC/2012/index.php) dataset for model training and validation. - -### Training - -- Distributed Training - - It is easy to reproduce the reported results with the pre-defined training recipe. For distributed training on multiple Ascend 910 devices, please run - - ```shell - # distributed training on multiple Ascend devices - mpirun -n 8 python train.py --config configs/edgenext/edgenext_small_ascend.yaml --data_dir /path/to/imagenet - ``` - - > If the script is executed by the root user, the `--allow-run-as-root` parameter must be added to `mpirun`. - - For detailed illustration of all hyper-parameters, please refer to [config.py](https://github.com/mindspore-lab/mindcv/blob/main/config.py). - - **Note:** As the global batch size (batch_size x num_devices) is an important hyper-parameter, it is recommended to keep the global batch size unchanged for reproduction or adjust the learning rate linearly to a new global batch size. - -- Standalone Training - - If you want to train or finetune the model on a smaller dataset without distributed training, please run: - - ```shell - # standalone training on a CPU/Ascend device - python train.py --config configs/edgenext/edgenext_small_ascend.yaml --data_dir /path/to/dataset --distribute False - ``` - -### Validation - -To validate the accuracy of the trained model, you can use `validate.py` and parse the checkpoint path with `--ckpt_path`. - -```shell -python validate.py -c configs/edgenext/edgenext_small_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt -``` - -### Deployment - -Please refer to the [deployment tutorial](https://mindspore-lab.github.io/mindcv/zh/tutorials/inference/) in MindCV. - -## References - - - -[1] Maaz M, Shaker A, Cholakkal H, et al. EdgeNeXt: efficiently amalgamated CNN-transformer architecture for Mobile vision applications[J]. arXiv preprint arXiv:2206.10589, 2022. diff --git a/mshub_res/assets/mindspore/2.5/edgenext_xx_small_imagenet2012.md b/mshub_res/assets/mindspore/2.5/edgenext_xx_small_imagenet2012.md index 88b88bbc669d4bb40e942fb532ee7c83e4b4c1a9..cdc2c771cdbb962f08d92ab92ba0bde1603af050 100644 --- a/mshub_res/assets/mindspore/2.5/edgenext_xx_small_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/edgenext_xx_small_imagenet2012.md @@ -75,12 +75,6 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. | ----------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | -------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------- | | edgenext_xx_small | 1.33 | 8 | 256 | 256x256 | O2 | 389s | 239.38 | 8555.43 | 70.64 | 89.75 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/edgenext/edgenext_xx_small_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/edgenext/edgenext_xx_small-cad13d2c-910v2.ckpt) | -Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode. - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ----------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | -------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------- | -| edgenext_xx_small | 1.33 | 8 | 256 | 256x256 | O2 | 311s | 191.24 | 10709.06 | 71.02 | 89.99 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/edgenext/edgenext_xx_small_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/edgenext/edgenext_xx_small-afc971fb.ckpt) | - ### Notes - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. diff --git a/mshub_res/assets/mindspore/2.5/efficientnet.md b/mshub_res/assets/mindspore/2.5/efficientnet.md deleted file mode 100644 index ca7b9710fe962a36acb4164271ee8a8b1f3d0309..0000000000000000000000000000000000000000 --- a/mshub_res/assets/mindspore/2.5/efficientnet.md +++ /dev/null @@ -1,152 +0,0 @@ -# efficientnet_b0 - ---- - -model-name: efficientnet_b0 - -backbone-name: efficientnet - -module-type: cv - -fine-tunable: True - -model-version: 2.5 - -train-dataset: ImageNet2012 - -evaluation: top1acc76.88 | top5acc93.28 - -author: MindSpore team - -update-time: 2025-03-10 - -repo-link: - -user-id: MindSpore - -used-for: inference - -mindspore-version: 2.5 - -asset: - -- file-format: ckpt - asset-link: - asset-sha256: f8d7aa2a - -license: Apache2.0 - -summary: efficientnet_b0 is used for cv - ---- - -# EfficientNet - - - -> [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946) - -## Introduction - - - - -Figure 1 shows the methods from three dimensions -- width, depth, resolution and compound to expand the model. Increasing the model -size solely would cause the model performance to sub-optimal solution. However, if three methods could be applied together into the model -, it is more likely to achieve optimal solution. By using neural architecture search, the best configurations for width scaling, depth scaling -and resolution scaling could be found. EfficientNet could achieve better model performance on ImageNet-1K dataset compared with previous methods.[[1](#references)] - -

- -

-

- Figure 1. Architecture of Efficientent [1] -

- -## Results - - - -Our reproduced model performance on ImageNet-1K is reported as follows. - -
- -| Model | Context | Top-1 (%) | Top-5 (%) | Params (M) | Recipe | Download | -| --------------- | --------- | --------- | --------- | ---------- | ---------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------- | -| efficientnet_b0 | D910x64-G | 76.89 | 93.16 | 5.33 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/efficientnet/efficientnet_b0_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/efficientnet/efficientnet_b0-103ec70c.ckpt) | -| efficientnet_b1 | D910x64-G | 78.95 | 94.34 | 7.86 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/efficientnet/efficientnet_b1_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/efficientnet/efficientnet_b1-f8c6b13f.ckpt) | - -
- -### Notes - -- Context: Training context denoted as {device}x{pieces}-{MS mode}, where mindspore mode can be G - graph mode or F - pynative mode with ms function. For example, D910x8-G is for training on 8 pieces of Ascend 910 NPU using graph mode. -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. - -## Quick Start - -### Preparation - -#### Installation - -Please refer to the [installation instruction](https://github.com/mindspore-lab/mindcv#installation) in MindCV. - -#### Dataset Preparation - -Please download the [ImageNet-1K](https://www.image-net.org/challenges/LSVRC/2012/index.php) dataset for model training and validation. - -### Training - - - -- Distributed Training - - It is easy to reproduce the reported results with the pre-defined training recipe. For distributed training on multiple Ascend 910 devices, please run - - ```shell - # distributed training on multiple Ascend devices - mpirun -n 64 python train.py --config configs/efficientnet/efficientnet_b0_ascend.yaml --data_dir /path/to/imagenet - ``` - - > If the script is executed by the root user, the `--allow-run-as-root` parameter must be added to `mpirun`. - - For detailed illustration of all hyper-parameters, please refer to [config.py](https://github.com/mindspore-lab/mindcv/blob/main/config.py). - - **Note:** As the global batch size (batch_size x num_devices) is an important hyper-parameter, it is recommended to keep the global batch size unchanged for reproduction or adjust the learning rate linearly to a new global batch size. - -- Standalone Training - - If you want to train or finetune the model on a smaller dataset without distributed training, please run: - - ```shell - # standalone training on a CPU/Ascend device - python train.py --config configs/efficientnet/efficientnet_b0_ascend.yaml --data_dir /path/to/dataset --distribute False - ``` - -### Validation - -To validate the accuracy of the trained model, you can use `validate.py` and parse the checkpoint path with `--ckpt_path`. - -```shell -python validate.py -c configs/efficientnet/efficientnet_b0_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt -``` - -### Deployment - -Please refer to the [deployment tutorial](https://mindspore-lab.github.io/mindcv/zh/tutorials/inference/) in MindCV. - -## References - - - -[1] Tan M, Le Q. Efficientnet: Rethinking model scaling for convolutional neural networks[C]//International conference on machine learning. PMLR, 2019: 6105-6114. diff --git a/mshub_res/assets/mindspore/2.5/efficientnet_b0_imagenet2012.md b/mshub_res/assets/mindspore/2.5/efficientnet_b0_imagenet2012.md index 4b464f72e030b1f863f24d37982b2bc98856c554..7bad3974922e631c40d0b8f505de11ca354003ce 100644 --- a/mshub_res/assets/mindspore/2.5/efficientnet_b0_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/efficientnet_b0_imagenet2012.md @@ -79,12 +79,6 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. | --------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------- | | efficientnet_b0 | 5.33 | 8 | 128 | 224x224 | O2 | 353s | 172.64 | 5931.42 | 76.88 | 93.28 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/efficientnet/efficientnet_b0_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/efficientnet/efficientnet_b0-f8d7aa2a-910v2.ckpt) | -Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode. - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| --------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------- | -| efficientnet_b0 | 5.33 | 8 | 128 | 224x224 | O2 | 203s | 172.78 | 5926.61 | 76.89 | 93.16 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/efficientnet/efficientnet_b0_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/efficientnet/efficientnet_b0-103ec70c.ckpt) | - ### Notes - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. diff --git a/mshub_res/assets/mindspore/2.5/ghostnet.md b/mshub_res/assets/mindspore/2.5/ghostnet.md deleted file mode 100644 index c10026f568d48a5fd1a2281652c27a6dcd39c23b..0000000000000000000000000000000000000000 --- a/mshub_res/assets/mindspore/2.5/ghostnet.md +++ /dev/null @@ -1,138 +0,0 @@ -# ghostnet_050 - ---- - -model-name: ghostnet_050 - -backbone-name: ghostnet - -module-type: cv - -fine-tunable: True - -model-version: 2.5 - -train-dataset: ImageNet2012 - -evaluation: top1acc65.84 | top5acc86.60 - -author: MindSpore team - -update-time: 2025-03-10 - -repo-link: - -user-id: MindSpore - -used-for: inference - -mindspore-version: 2.5 - -asset: - -- file-format: ckpt - asset-link: - asset-sha256: ae7771cb - -license: Apache2.0 - -summary: ghostnet is used for cv - ---- - -# ghostnet_050 - -> [GhostNet: More Features from Cheap Operations](https://arxiv.org/abs/1911.11907) - -## Introduction - -The redundancy in feature maps is an important characteristic of those successful CNNs, but has rarely been -investigated in neural architecture design. This paper proposes a novel Ghost module to generate more feature maps from -cheap operations. Based on a set of intrinsic feature maps, the authors apply a series of linear transformations with -cheap cost to generate many ghost feature maps that could fully reveal information underlying intrinsic features. The -proposed Ghost module can be taken as a plug-and-play component to upgrade existing convolutional neural networks. -Ghost bottlenecks are designed to stack Ghost modules, and then the lightweight GhostNet can be easily -established. Experiments conducted on benchmarks demonstrate that the Ghost module is an impressive alternative of -convolution layers in baseline models, and GhostNet can achieve higher recognition performance (e.g. 75.7% top-1 -accuracy) than MobileNetV3 with similar computational cost on the ImageNet ILSVRC-2012 classification -dataset.[[1](#references)] - -

- -

-

- Figure 1. Architecture of GhostNet [1] -

- -## Results - -Our reproduced model performance on ImageNet-1K is reported as follows. - -
- -| Model | Context | Top-1 (%) | Top-5 (%) | Params (M) | Recipe | Download | -| ------------ | -------- | --------- | --------- | ---------- | --------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------- | -| ghostnet_050 | D910x8-G | 66.03 | 86.64 | 2.60 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/ghostnet/ghostnet_050_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/ghostnet/ghostnet_050-85b91860.ckpt) | -| ghostnet_100 | D910x8-G | 73.78 | 91.66 | 5.20 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/ghostnet/ghostnet_100_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/ghostnet/ghostnet_100-bef8025a.ckpt) | -| ghostnet_130 | D910x8-G | 75.50 | 92.56 | 7.39 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/ghostnet/ghostnet_130_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/ghostnet/ghostnet_130-cf4c235c.ckpt) | - -
- -### Notes - -- Context: Training context denoted as {device}x{pieces}-{MS mode}, where mindspore mode can be G - graph mode or F - pynative mode with ms function. For example, D910x8-G is for training on 8 pieces of Ascend 910 NPU using graph mode. -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. - -## Quick Start - -### Preparation - -#### Installation - -Please refer to the [installation instruction](https://github.com/mindspore-ecosystem/mindcv#installation) in MindCV. - -#### Dataset Preparation - -Please download the [ImageNet-1K](https://www.image-net.org/challenges/LSVRC/2012/index.php) dataset for model training and validation. - -### Training - -- Distributed Training - - It is easy to reproduce the reported results with the pre-defined training recipe. For distributed training on multiple Ascend 910 devices, please run - - ```shell - # distributed training on multiple Ascend devices - mpirun -n 8 python train.py --config configs/ghostnet/ghostnet_100_ascend.yaml --data_dir /path/to/imagenet - ``` - - > If the script is executed by the root user, the `--allow-run-as-root` parameter must be added to `mpirun`. - - For detailed illustration of all hyper-parameters, please refer to [config.py](https://github.com/mindspore-lab/mindcv/blob/main/config.py). - - **Note:** As the global batch size (batch_size x num_devices) is an important hyper-parameter, it is recommended to keep the global batch size unchanged for reproduction or adjust the learning rate linearly to a new global batch size. - -- Standalone Training - - If you want to train or finetune the model on a smaller dataset without distributed training, please run: - - ```shell - # standalone training on a CPU/Ascend device - python train.py --config configs/ghostnet/ghostnet_100_ascend.yaml --data_dir /path/to/dataset --distribute False - ``` - -### Validation - -To validate the accuracy of the trained model, you can use `validate.py` and parse the checkpoint path with `--ckpt_path`. - -```shell -python validate.py -c configs/ghostnet/ghostnet_100_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt -``` - -### Deployment - -Please refer to the [deployment tutorial](https://mindspore-lab.github.io/mindcv/zh/tutorials/inference/) in MindCV. - -## References - -[1] Han K, Wang Y, Tian Q, et al. Ghostnet: More features from cheap operations[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020: 1580-1589. diff --git a/mshub_res/assets/mindspore/2.5/ghostnet_050_imagenet2012.md b/mshub_res/assets/mindspore/2.5/ghostnet_050_imagenet2012.md index 32443f7dd6e74b297ce17f1b910842d9415c9559..1383958161ce89758540f6e374e2174071bd1ef2 100644 --- a/mshub_res/assets/mindspore/2.5/ghostnet_050_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/ghostnet_050_imagenet2012.md @@ -78,12 +78,6 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. _coming soon_ -Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode. - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ------------ | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------- | -| ghostnet_050 | 2.60 | 8 | 128 | 224x224 | O2 | 383s | 211.13 | 4850.09 | 66.03 | 86.64 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/ghostnet/ghostnet_050_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/ghostnet/ghostnet_050-85b91860.ckpt) | - ### Notes - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. diff --git a/mshub_res/assets/mindspore/2.5/googlenet.md b/mshub_res/assets/mindspore/2.5/googlenet.md deleted file mode 100644 index 8826aa7408d2042a2280cb5483e1105a866ed4d2..0000000000000000000000000000000000000000 --- a/mshub_res/assets/mindspore/2.5/googlenet.md +++ /dev/null @@ -1,132 +0,0 @@ -# googlenet - ---- - -model-name: googlenet - -backbone-name: googlenet - -module-type: cv-classification - -fine-tunable: True - -model-version: 2.5 - -train-dataset: ImageNet2012 - -evaluation: top1acc72.89 | top5acc90.89 - -author: MindSpore team - -update-time: 2025-03-10 - -repo-link: - -user-id: MindSpore - -used-for: inference - -mindspore-version: 2.5 - -asset: - -- file-format: ckpt - asset-link: - asset-sha256: de74c31d - -license: Apache2.0 - -summary: googlenet is used for cv - ---- - -# GoogLeNet - -> [GoogLeNet: Going Deeper with Convolutions](https://arxiv.org/abs/1409.4842) - -## Introduction - -GoogLeNet is a new deep learning structure proposed by Christian Szegedy in 2014. Prior to this, AlexNet, VGG and other -structures achieved better training effects by increasing the depth (number of layers) of the network, but the increase -in the number of layers It will bring many negative effects, such as overfit, gradient disappearance, gradient -explosion, etc. The proposal of inception improves the training results from another perspective: it can use computing -resources more efficiently, and can extract more features under the same amount of computing, thereby improving the -training results.[[1](#references)] - -

- -

-

- Figure 1. Architecture of GoogLeNet [1] -

- -## Results - -Our reproduced model performance on ImageNet-1K is reported as follows. - -
- -| Model | Context | Top-1 (%) | Top-5 (%) | Params (M) | Recipe | Download | -| --------- | -------- | --------- | --------- | ---------- | ------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------- | -| googlenet | D910x8-G | 72.68 | 90.89 | 6.99 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/googlenet/googlenet_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/googlenet/googlenet-5552fcd3.ckpt) | - -
- -### Notes - -- Context: Training context denoted as {device}x{pieces}-{MS mode}, where mindspore mode can be G - graph mode or F - pynative mode with ms function. For example, D910x8-G is for training on 8 pieces of Ascend 910 NPU using graph mode. -- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. - -## Quick Start - -### Preparation - -#### Installation - -Please refer to the [installation instruction](https://github.com/mindspore-ecosystem/mindcv#installation) in MindCV. - -#### Dataset Preparation - -Please download the [ImageNet-1K](https://www.image-net.org/challenges/LSVRC/2012/index.php) dataset for model training and validation. - -### Training - -- Distributed Training - - It is easy to reproduce the reported results with the pre-defined training recipe. For distributed training on multiple Ascend 910 devices, please run - - ```shell - # distributed training on multiple Ascend devices - mpirun -n 8 python train.py --config configs/googlenet/googlenet_ascend.yaml --data_dir /path/to/imagenet - ``` - - > If the script is executed by the root user, the `--allow-run-as-root` parameter must be added to `mpirun`. - - For detailed illustration of all hyper-parameters, please refer to [config.py](https://github.com/mindspore-lab/mindcv/blob/main/config.py). - - **Note:** As the global batch size (batch_size x num_devices) is an important hyper-parameter, it is recommended to keep the global batch size unchanged for reproduction or adjust the learning rate linearly to a new global batch size. - -- Standalone Training - - If you want to train or finetune the model on a smaller dataset without distributed training, please run: - - ```shell - # standalone training on a CPU/Ascend device - python train.py --config configs/googlenet/googlenet_ascend.yaml --data_dir /path/to/dataset --distribute False - ``` - -### Validation - -To validate the accuracy of the trained model, you can use `validate.py` and parse the checkpoint path with `--ckpt_path`. - -```shell -python validate.py -c configs/googlenet/googlenet_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt -``` - -### Deployment - -Please refer to the [deployment tutorial](https://mindspore-lab.github.io/mindcv/zh/tutorials/inference/) in MindCV. - -## References - -[1] Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 1-9. diff --git a/mshub_res/assets/mindspore/2.5/googlenet_imagenet2012.md b/mshub_res/assets/mindspore/2.5/googlenet_imagenet2012.md index 736797bf8e2a51dd01d92e03550b041ce90d76e2..51b08b648070c63b6651a38347787245c3416321 100644 --- a/mshub_res/assets/mindspore/2.5/googlenet_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/googlenet_imagenet2012.md @@ -6,7 +6,7 @@ model-name: googlenet backbone-name: googlenet -module-type: cv-classification +module-type: cv fine-tunable: True @@ -76,12 +76,6 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. | ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------- | | googlenet | 6.99 | 8 | 32 | 224x224 | O2 | 113s | 23.5 | 10893.62 | 72.89 | 90.89 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/googlenet/googlenet_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/googlenet/googlenet-de74c31d-910v2.ckpt) | -Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode. - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------ | -| googlenet | 6.99 | 8 | 32 | 224x224 | O2 | 72s | 21.40 | 11962.62 | 72.68 | 90.89 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/googlenet/googlenet_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/googlenet/googlenet-5552fcd3.ckpt) | - ### Notes - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. diff --git a/mshub_res/assets/mindspore/2.5/hrnet_w32_imagenet2012.md b/mshub_res/assets/mindspore/2.5/hrnet_w32_imagenet2012.md index 44852c61a13aefa53a90fb735c0a1ab677dabc30..6a3cfb1c7ef07cbb28db19cc19ef8906b274b75c 100644 --- a/mshub_res/assets/mindspore/2.5/hrnet_w32_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/hrnet_w32_imagenet2012.md @@ -77,12 +77,6 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. | ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------- | | hrnet_w32 | 41.30 | 8 | 128 | 224x224 | O2 | 1069s | 238.03 | 4301.98 | 80.66 | 95.30 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/hrnet/hrnet_w32_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/hrnet/hrnet_w32-e616cdcb-910v2.ckpt) | -Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode. - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------- | -| hrnet_w32 | 41.30 | 128 | 8 | 224x224 | O2 | 1312s | 279.10 | 3668.94 | 80.64 | 95.44 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/hrnet/hrnet_w32_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/hrnet/hrnet_w32-cc4fbd91.ckpt) | - ### Notes - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. diff --git a/mshub_res/assets/mindspore/2.5/inception_v3_imagenet2012.md b/mshub_res/assets/mindspore/2.5/inception_v3_imagenet2012.md index 2ed4f955d7b42a3a6ef13b9d9d1619fadafc0324..f5c8dac7c747d121574bac7aa51fa8ddef775b6c 100644 --- a/mshub_res/assets/mindspore/2.5/inception_v3_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/inception_v3_imagenet2012.md @@ -77,12 +77,6 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. | ------------ | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------- | | inception_v3 | 27.20 | 8 | 32 | 299x299 | O2 | 172s | 70.83 | 3614.29 | 79.25 | 94.47 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/inceptionv3/inception_v3_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/inception_v3/inception_v3-61a8e9ed-910v2.ckpt) | -Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode. - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ------------ | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------ | -| inception_v3 | 27.20 | 8 | 32 | 299x299 | O2 | 120s | 76.42 | 3349.91 | 79.11 | 94.40 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/inceptionv3/inception_v3_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/inception_v3/inception_v3-38f67890.ckpt) | - ### Notes - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. diff --git a/mshub_res/assets/mindspore/2.5/inception_v4_imagenet2012.md b/mshub_res/assets/mindspore/2.5/inception_v4_imagenet2012.md index 2efb625a59f20b14792a85781e631daed22fc71e..af9981208d2da2fadd3e8256322884dde3aeeb08 100644 --- a/mshub_res/assets/mindspore/2.5/inception_v4_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/inception_v4_imagenet2012.md @@ -74,12 +74,6 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. | ------------ | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------- | | inception_v4 | 42.74 | 8 | 32 | 299x299 | O2 | 263s | 80.97 | 3161.66 | 80.98 | 95.25 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/inceptionv4/inception_v4_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/inception_v4/inception_v4-56e798fc-910v2.ckpt) | -Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode. - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ------------ | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------ | -| inception_v4 | 42.74 | 8 | 32 | 299x299 | O2 | 177s | 76.19 | 3360.02 | 80.88 | 95.34 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/inceptionv4/inception_v4_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/inception_v4/inception_v4-db9c45b3.ckpt) | - ### Notes - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. diff --git a/mshub_res/assets/mindspore/2.5/mixnet_s_imagenet2012.md b/mshub_res/assets/mindspore/2.5/mixnet_s_imagenet2012.md index 73e9021da9a9b2607adc84cb7854179396026898..9d7d68660daef4b31a44490838bc9197fc5a09aa 100644 --- a/mshub_res/assets/mindspore/2.5/mixnet_s_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/mixnet_s_imagenet2012.md @@ -76,12 +76,6 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. | ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------- | | mixnet_s | 4.17 | 8 | 128 | 224x224 | O2 | 706s | 228.03 | 4490.64 | 75.58 | 95.54 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mixnet/mixnet_s_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mixnet/mixnet_s-fe4fcc63-910v2.ckpt) | -Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode. - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------- | -| mixnet_s | 4.17 | 8 | 128 | 224x224 | O2 | 556s | 252.49 | 4055.61 | 75.52 | 92.52 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mixnet/mixnet_s_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/mixnet/mixnet_s-2a5ef3a3.ckpt) | - ### Notes - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. diff --git a/mshub_res/assets/mindspore/2.5/mnasnet_075_imagenet2012.md b/mshub_res/assets/mindspore/2.5/mnasnet_075_imagenet2012.md index 9f26945ff9adbc303cda5311d45f15c6a857878f..33e98748920a441164caed03a52d7534ab9fc22e 100644 --- a/mshub_res/assets/mindspore/2.5/mnasnet_075_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/mnasnet_075_imagenet2012.md @@ -71,12 +71,6 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. | ----------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | -------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------- | | mnasnet_075 | 3.20 | 8 | 256 | 224x224 | O2 | 144s | 175.85 | 11646.29 | 71.77 | 90.52 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mnasnet/mnasnet_0.75_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mnasnet/mnasnet_075-083b2bc4-910v2.ckpt) | -Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode. - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ----------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | -------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------ | -| mnasnet_075 | 3.20 | 8 | 256 | 224x224 | O2 | 140s | 165.43 | 12379.86 | 71.81 | 90.53 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mnasnet/mnasnet_0.75_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/mnasnet/mnasnet_075-465d366d.ckpt) | - ### Notes - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. diff --git a/mshub_res/assets/mindspore/2.5/mobilenet_v1_025_imagenet2012.md b/mshub_res/assets/mindspore/2.5/mobilenet_v1_025_imagenet2012.md index 3444118a0a3915db5c6067be5423fe7c36c8d650..1cab67786871a994e438aabb64cc37d702cd6a71 100644 --- a/mshub_res/assets/mindspore/2.5/mobilenet_v1_025_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/mobilenet_v1_025_imagenet2012.md @@ -71,12 +71,6 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. | ---------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ----------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------- | | mobilenet_v1_025 | 0.47 | 8 | 64 | 224x224 | O2 | 195s | 47.47 | 10785.76 | 54.05 | 77.74 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv1/mobilenet_v1_0.25_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mobilenet/mobilenetv1/mobilenet_v1_025-cbe3d3b3-910v2.ckpt) | -Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode. - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ----------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- | -| mobilenet_v1_025 | 0.47 | 8 | 64 | 224x224 | O2 | 89s | 42.43 | 12066.93 | 53.87 | 77.66 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv1/mobilenet_v1_0.25_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenetv1/mobilenet_v1_025-d3377fba.ckpt) | - ### Notes - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. diff --git a/mshub_res/assets/mindspore/2.5/mobilenet_v2_075_imagenet2012.md b/mshub_res/assets/mindspore/2.5/mobilenet_v2_075_imagenet2012.md index fa9a35bf0e5193d79dc3a6aa2db87e28f74d8a8d..c0feea0b7c4d827ab712177f0a626717ae538697 100644 --- a/mshub_res/assets/mindspore/2.5/mobilenet_v2_075_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/mobilenet_v2_075_imagenet2012.md @@ -73,12 +73,6 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. | ---------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ----------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------- | | mobilenet_v2_075 | 2.66 | 8 | 256 | 224x224 | O2 | 233s | 174.65 | 11726.31 | 69.73 | 89.35 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv2/mobilenet_v2_0.75_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mobilenet/mobilenetv2/mobilenet_v2_075-755932c4-910v2.ckpt) | -Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode. - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ----------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- | -| mobilenet_v2_075 | 2.66 | 8 | 256 | 224x224 | O2 | 164s | 155.94 | 13133.26 | 69.98 | 89.32 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv2/mobilenet_v2_0.75_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenetv2/mobilenet_v2_075-bd7bd4c4.ckpt) | - ### Notes - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. diff --git a/mshub_res/assets/mindspore/2.5/mobilenet_v3_large_100_imagenet2012.md b/mshub_res/assets/mindspore/2.5/mobilenet_v3_large_100_imagenet2012.md index f99a4452f80ec45bb563e6ce612929d8c33cfc5f..df869ce84ff7687b0905c06d72dd2ddc01d3f7ef 100644 --- a/mshub_res/assets/mindspore/2.5/mobilenet_v3_large_100_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/mobilenet_v3_large_100_imagenet2012.md @@ -74,13 +74,6 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. | mobilenet_v3_small_100 | 2.55 | 8 | 75 | 224x224 | O2 | 184s | 52.38 | 11454.75 | 68.07 | 87.77 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv3/mobilenet_v3_small_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mobilenet/mobilenetv3/mobilenet_v3_small_100-6fa3c17d-910v2.ckpt) | | mobilenet_v3_large_100 | 5.51 | 8 | 75 | 224x224 | O2 | 354s | 55.89 | 10735.37 | 75.59 | 92.57 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv3/mobilenet_v3_large_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mobilenet/mobilenetv3/mobilenet_v3_large_100-bd4e7bdc-910v2.ckpt) | -Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode. - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------- | -| mobilenet_v3_small_100 | 2.55 | 8 | 75 | 224x224 | O2 | 145s | 48.14 | 12463.65 | 68.10 | 87.86 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv3/mobilenet_v3_small_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenetv3/mobilenet_v3_small_100-509c6047.ckpt) | -| mobilenet_v3_large_100 | 5.51 | 8 | 75 | 224x224 | O2 | 271s | 47.49 | 12634.24 | 75.23 | 92.31 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv3/mobilenet_v3_large_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenetv3/mobilenet_v3_large_100-1279ad5f.ckpt) | - ### Notes - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. diff --git a/mshub_res/assets/mindspore/2.5/mobilenet_v3_small_100_imagenet2012.md b/mshub_res/assets/mindspore/2.5/mobilenet_v3_small_100_imagenet2012.md index 6008252eea24660b9cfa457685c42dd0e76a1d11..b8846d121f26f11c230ddf3252a763f0a39a691f 100644 --- a/mshub_res/assets/mindspore/2.5/mobilenet_v3_small_100_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/mobilenet_v3_small_100_imagenet2012.md @@ -74,13 +74,6 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. | mobilenet_v3_small_100 | 2.55 | 8 | 75 | 224x224 | O2 | 184s | 52.38 | 11454.75 | 68.07 | 87.77 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv3/mobilenet_v3_small_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mobilenet/mobilenetv3/mobilenet_v3_small_100-6fa3c17d-910v2.ckpt) | | mobilenet_v3_large_100 | 5.51 | 8 | 75 | 224x224 | O2 | 354s | 55.89 | 10735.37 | 75.59 | 92.57 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv3/mobilenet_v3_large_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mobilenet/mobilenetv3/mobilenet_v3_large_100-bd4e7bdc-910v2.ckpt) | -Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode. - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------- | -| mobilenet_v3_small_100 | 2.55 | 8 | 75 | 224x224 | O2 | 145s | 48.14 | 12463.65 | 68.10 | 87.86 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv3/mobilenet_v3_small_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenetv3/mobilenet_v3_small_100-509c6047.ckpt) | -| mobilenet_v3_large_100 | 5.51 | 8 | 75 | 224x224 | O2 | 271s | 47.49 | 12634.24 | 75.23 | 92.31 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilenetv3/mobilenet_v3_large_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/mobilenet/mobilenetv3/mobilenet_v3_large_100-1279ad5f.ckpt) | - ### Notes - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. diff --git a/mshub_res/assets/mindspore/2.5/mobilevit_xx_small_imagenet2012.md b/mshub_res/assets/mindspore/2.5/mobilevit_xx_small_imagenet2012.md index 7de01536cf24575aec323d631e08bab77262f5e4..2f14c08dc196a244595ceb2a1aa9aec7f9e79bab 100644 --- a/mshub_res/assets/mindspore/2.5/mobilevit_xx_small_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/mobilevit_xx_small_imagenet2012.md @@ -71,12 +71,6 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. | ------------------ | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------- | | mobilevit_xx_small | 1.27 | 8 | 64 | 256x256 | O2 | 437s | 67.24 | 7614.52 | 67.11 | 87.85 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilevit/mobilevit_xx_small_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/mobilevit/mobilevit_xx_small-6f2745c3-910v2.ckpt) | -Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode. - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ------------------ | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------- | -| mobilevit_xx_small | 1.27 | 64 | 8 | 256x256 | O2 | 301s | 53.52 | 9566.52 | 68.91 | 88.91 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mobilevit/mobilevit_xx_small_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/mobilevit/mobilevit_xx_small-af9da8a0.ckpt) | - ### Notes - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. diff --git a/mshub_res/assets/mindspore/2.5/nasnet_a_4x1056_imagenet2012.md b/mshub_res/assets/mindspore/2.5/nasnet_a_4x1056_imagenet2012.md index e9d32f16fb169e7f03d6fb08be074de88c7595d3..1ee6aadf7004ab8c68adea6a4b757cda0fe04590 100644 --- a/mshub_res/assets/mindspore/2.5/nasnet_a_4x1056_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/nasnet_a_4x1056_imagenet2012.md @@ -79,12 +79,6 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. | --------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------ | | nasnet_a_4x1056 | 5.33 | 8 | 256 | 224x224 | O2 | 800s | 364.35 | 5620.97 | 74.12 | 91.36 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/nasnet/nasnet_a_4x1056_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/nasnet/nasnet_a_4x1056-015ba575c-910v2.ckpt) | -Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode. - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| --------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------- | -| nasnet_a_4x1056 | 5.33 | 8 | 256 | 224x224 | O2 | 656s | 330.89 | 6189.37 | 73.65 | 91.25 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/nasnet/nasnet_a_4x1056_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/nasnet/nasnet_a_4x1056-0fbb5cdd.ckpt) | - ### Notes - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. diff --git a/mshub_res/assets/mindspore/2.5/pit_ti_imagenet2012.md b/mshub_res/assets/mindspore/2.5/pit_ti_imagenet2012.md index 7fd461f45fe5b8feaa2e6a71bf3229d33b31b2ed..2399e46caa17c761c014e101dfa172e24e1b28f8 100644 --- a/mshub_res/assets/mindspore/2.5/pit_ti_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/pit_ti_imagenet2012.md @@ -72,12 +72,6 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. | ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- | | pit_ti | 4.85 | 8 | 128 | 224x224 | O2 | 212s | 266.47 | 3842.83 | 73.26 | 91.57 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/pit/pit_ti_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/pit/pit_ti-33466a0d-910v2.ckpt) | -Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode. - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------- | -| pit_ti | 4.85 | 8 | 128 | 224x224 | O2 | 192s | 271.50 | 3771.64 | 72.96 | 91.33 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/pit/pit_ti_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/pit/pit_ti-e647a593.ckpt) | - ### Notes - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. diff --git a/mshub_res/assets/mindspore/2.5/poolformer_s12_imagenet2012.md b/mshub_res/assets/mindspore/2.5/poolformer_s12_imagenet2012.md index 7b7271ac24b2407b27f628067456c6e66cfe190e..e436037e86f8c523ee2acffb8d214d44563b79b1 100644 --- a/mshub_res/assets/mindspore/2.5/poolformer_s12_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/poolformer_s12_imagenet2012.md @@ -70,12 +70,6 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. | -------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------- | | poolformer_s12 | 11.92 | 8 | 128 | 224x224 | O2 | 177s | 211.81 | 4834.52 | 77.49 | 93.55 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/poolformer/poolformer_s12_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/poolformer/poolformer_s12-c7e14eea-910v2.ckpt) | -Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode. - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| -------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------ | -| poolformer_s12 | 11.92 | 8 | 128 | 224x224 | O2 | 118s | 220.13 | 4651.80 | 77.33 | 93.34 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/poolformer/poolformer_s12_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/poolformer/poolformer_s12-5be5c4e4.ckpt) | - ### Notes - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. diff --git a/mshub_res/assets/mindspore/2.5/pvt_tiny_imagenet2012.md b/mshub_res/assets/mindspore/2.5/pvt_tiny_imagenet2012.md index 4aaf5798a74fb08aa0e052360782341c83b4ec99..3d5fcc2a2289bbe98882c04cf34125da8bc8ee5d 100644 --- a/mshub_res/assets/mindspore/2.5/pvt_tiny_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/pvt_tiny_imagenet2012.md @@ -70,12 +70,6 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. | ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------- | | pvt_tiny | 13.23 | 8 | 128 | 224x224 | O2 | 212s | 237.5 | 4311.58 | 74.88 | 92.12 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/pvt/pvt_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/pvt/pvt_tiny-6676051f-910v2.ckpt) | -Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode. - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------- | -| pvt_tiny | 13.23 | 8 | 128 | 224x224 | O2 | 192s | 229.63 | 4459.35 | 74.81 | 92.18 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/pvt/pvt_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/pvt/pvt_tiny-6abb953d.ckpt) | - ### Notes - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. diff --git a/mshub_res/assets/mindspore/2.5/pvt_v2_b0_imagenet2012.md b/mshub_res/assets/mindspore/2.5/pvt_v2_b0_imagenet2012.md index c57fc1efd198c7baa3b119faad42023dc4fb0c42..d864155982973b4b790635a7709140838770854a 100644 --- a/mshub_res/assets/mindspore/2.5/pvt_v2_b0_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/pvt_v2_b0_imagenet2012.md @@ -75,12 +75,6 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. | ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------- | | pvt_v2_b0 | 3.67 | 8 | 128 | 224x224 | O2 | 323s | 255.76 | 4003.75 | 71.25 | 90.50 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/pvtv2/pvt_v2_b0_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/pvt_v2/pvt_v2_b0-d9cd9d6a-910v2.ckpt) | -Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode. - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------- | -| pvt_v2_b0 | 3.67 | 8 | 128 | 224x224 | O2 | 269s | 269.38 | 3801.32 | 71.50 | 90.60 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/pvtv2/pvt_v2_b0_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/pvt_v2/pvt_v2_b0-1c4f6683.ckpt) | - ### Notes - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. diff --git a/mshub_res/assets/mindspore/2.5/regnet_x_800mf_imagenet2012.md b/mshub_res/assets/mindspore/2.5/regnet_x_800mf_imagenet2012.md index abdceb09af56ba50ebba00b7f1e8d2d88b283916..b552f5cfa7c2e823f4dd36734e52c2bdb44fc179 100644 --- a/mshub_res/assets/mindspore/2.5/regnet_x_800mf_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/regnet_x_800mf_imagenet2012.md @@ -79,12 +79,6 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. | -------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | --------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------- | | regnet_x_800mf | 7.26 | 8 | 64 | 224x224 | O2 | 228s | 50.74 | 10090.66 | 76.11 | 93.00 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/regnet/regnet_x_800mf_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/regnet/regnet_x_800mf-68fe1cca-910v2.ckpt) | -Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode. - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| -------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | --------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------- | -| regnet_x_800mf | 7.26 | 8 | 64 | 224x224 | O2 | 99s | 42.49 | 12049.89 | 76.04 | 92.97 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/regnet/regnet_x_800mf_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/regnet/regnet_x_800mf-617227f4.ckpt) | - ### Notes - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. diff --git a/mshub_res/assets/mindspore/2.5/repvgg_a0_imagenet2012.md b/mshub_res/assets/mindspore/2.5/repvgg_a0_imagenet2012.md index 059530547d8fa4084273fc3d9053fbdde0393e45..3fdc8995906e0df7b299d1c54535a61af007625e 100644 --- a/mshub_res/assets/mindspore/2.5/repvgg_a0_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/repvgg_a0_imagenet2012.md @@ -82,13 +82,6 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. | repvgg_a0 | 9.13 | 8 | 32 | 224x224 | O2 | 76s | 24.12 | 10613.60 | 72.29 | 90.78 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/repvgg/repvgg_a0_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/repvgg/repvgg_a0-b67a9f15-910v2.ckpt) | | repvgg_a1 | 14.12 | 8 | 32 | 224x224 | O2 | 81s | 28.29 | 9096.13 | 73.68 | 91.51 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/repvgg/repvgg_a1_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/repvgg/repvgg_a1-a40aa623-910v2.ckpt) | -Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode. - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ---------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------- | -| repvgg_a0 | 9.13 | 8 | 32 | 224x224 | O2 | 50s
| 20.58 | 12439.26 | 72.19 | 90.75 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/repvgg/repvgg_a0_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/repvgg/repvgg_a0-6e71139d.ckpt) | -| repvgg_a1 | 14.12 | 8 | 32 | 224x224 | O2 | 29s | 20.70 | 12367.15 | 74.19 | 91.89 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/repvgg/repvgg_a1_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/repvgg/repvgg_a1-539513ac.ckpt) | - ### Notes - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. diff --git a/mshub_res/assets/mindspore/2.5/repvgg_a1_imagenet2012.md b/mshub_res/assets/mindspore/2.5/repvgg_a1_imagenet2012.md index 93b9731c902203f0bb7a49cd0ed7e474c8ce2f60..2349f6ef3dc92b6d28721aa2b78c6860bab80d23 100644 --- a/mshub_res/assets/mindspore/2.5/repvgg_a1_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/repvgg_a1_imagenet2012.md @@ -82,13 +82,6 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. | repvgg_a0 | 9.13 | 8 | 32 | 224x224 | O2 | 76s | 24.12 | 10613.60 | 72.29 | 90.78 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/repvgg/repvgg_a0_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/repvgg/repvgg_a0-b67a9f15-910v2.ckpt) | | repvgg_a1 | 14.12 | 8 | 32 | 224x224 | O2 | 81s | 28.29 | 9096.13 | 73.68 | 91.51 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/repvgg/repvgg_a1_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/repvgg/repvgg_a1-a40aa623-910v2.ckpt) | -Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode. - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ---------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------- | -| repvgg_a0 | 9.13 | 8 | 32 | 224x224 | O2 | 50s
| 20.58 | 12439.26 | 72.19 | 90.75 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/repvgg/repvgg_a0_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/repvgg/repvgg_a0-6e71139d.ckpt) | -| repvgg_a1 | 14.12 | 8 | 32 | 224x224 | O2 | 29s | 20.70 | 12367.15 | 74.19 | 91.89 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/repvgg/repvgg_a1_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/repvgg/repvgg_a1-539513ac.ckpt) | - ### Notes - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. diff --git a/mshub_res/assets/mindspore/2.5/res2net50_imagenet2012.md b/mshub_res/assets/mindspore/2.5/res2net50_imagenet2012.md index 8f2aed883db1cb3b97a6d9eb4feec849db47f919..cd595c1db38c64a151383e509d6c37639516fde5 100644 --- a/mshub_res/assets/mindspore/2.5/res2net50_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/res2net50_imagenet2012.md @@ -76,12 +76,6 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. | ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------ | | res2net50 | 25.76 | 8 | 32 | 224x224 | O2 | 174s | 39.6 | 6464.65 | 79.33 | 94.64 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/res2net/res2net_50_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/res2net/res2net50-aa758355-910v2.ckpt) | -Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode. - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------- | -| res2net50 | 25.76 | 8 | 32 | 224x224 | O2 | 119s | 39.68 | 6451.61 | 79.35 | 94.64 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/res2net/res2net_50_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/res2net/res2net50-f42cf71b.ckpt) | - ### Notes - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. diff --git a/mshub_res/assets/mindspore/2.5/resnet50_imagenet2012.md b/mshub_res/assets/mindspore/2.5/resnet50_imagenet2012.md index dbb256a61e524b99db2a9d06de9d41a7192c8da6..753ad390582e83941f4e61d31125ea180527873d 100644 --- a/mshub_res/assets/mindspore/2.5/resnet50_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/resnet50_imagenet2012.md @@ -74,12 +74,6 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. | ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------- | | resnet50 | 25.61 | 8 | 32 | 224x224 | O2 | 77s | 31.9 | 8025.08 | 76.76 | 93.31 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/resnet/resnet_50_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/resnet/resnet50-f369a08d-910v2.ckpt) | -Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode. - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------- | -| resnet50 | 25.61 | 8 | 32 | 224x224 | O2 | 43s | 31.41 | 8150.27 | 76.69 | 93.50 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/resnet/resnet_50_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/resnet/resnet50-e0733ab8.ckpt) | - ### Notes - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. diff --git a/mshub_res/assets/mindspore/2.5/resnetv2_50_imagenet2012.md b/mshub_res/assets/mindspore/2.5/resnetv2_50_imagenet2012.md index a1b2fc79ebc14634f3d878782cabeee53f957fb1..40647f589ff804ee1d86377e105b22f3dfee54f5 100644 --- a/mshub_res/assets/mindspore/2.5/resnetv2_50_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/resnetv2_50_imagenet2012.md @@ -73,12 +73,6 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. | ----------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | -------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------- | | resnetv2_50 | 25.60 | 8 | 32 | 224x224 | O2 | 120s | 32.19 | 7781.16 | 77.03 | 93.29 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/resnetv2/resnetv2_50_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/resnetv2/resnetv2_50-a0b9f7f8-910v2.ckpt) | -Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode. - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ----------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | -------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------- | -| resnetv2_50 | 25.60 | 8 | 32 | 224x224 | O2 | 52s | 32.66 | 7838.33 | 76.90 | 93.37 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/resnetv2/resnetv2_50_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/resnetv2/resnetv2_50-3c2f143b.ckpt) | - ### Notes - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. diff --git a/mshub_res/assets/mindspore/2.5/resnext50_32x4d_imagenet2012.md b/mshub_res/assets/mindspore/2.5/resnext50_32x4d_imagenet2012.md index dfcca9e620c539aa973d716a08f404c88307c440..59b2bbb018a6e425f6911e05e56a6b1ca671fbc9 100644 --- a/mshub_res/assets/mindspore/2.5/resnext50_32x4d_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/resnext50_32x4d_imagenet2012.md @@ -77,12 +77,6 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. | --------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ----------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------ | | resnext50_32x4d | 25.10 | 8 | 32 | 224x224 | O2 | 156s | 44.61 | 5738.62 | 78.64 | 94.18 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/resnext/resnext50_32x4d_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/resnext/resnext50_32x4d-988f75bc-910v2.ckpt) | -Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode. - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| --------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ----------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------- | -| resnext50_32x4d | 25.10 | 8 | 32 | 224x224 | O2 | 49s | 37.22 | 6878.02 | 78.53 | 94.10 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/resnext/resnext50_32x4d_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/resnext/resnext50_32x4d-af8aba16.ckpt) | - ### Notes - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. diff --git a/mshub_res/assets/mindspore/2.5/rexnet_09_imagenet2012.md b/mshub_res/assets/mindspore/2.5/rexnet_09_imagenet2012.md index 6a7736ec887e76ee2458015ab3fcf301404a889e..6f5d0f3d190445f5d9f22afefb92865bc9ed9a6c 100644 --- a/mshub_res/assets/mindspore/2.5/rexnet_09_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/rexnet_09_imagenet2012.md @@ -66,10 +66,6 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. _coming soon_ -Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode. - -_coming soon_ - ### Notes - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. diff --git a/mshub_res/assets/mindspore/2.5/seresnet18_imagenet2012.md b/mshub_res/assets/mindspore/2.5/seresnet18_imagenet2012.md index 53da02da0a25f23d7d7fc4816a092fc3943c5712..1b5c01ee09fbefc43ed93875e44fd649451a2bc5 100644 --- a/mshub_res/assets/mindspore/2.5/seresnet18_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/seresnet18_imagenet2012.md @@ -76,12 +76,6 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. | ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ---------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------- | | seresnet18 | 11.80 | 8 | 64 | 224x224 | O2 | 90s | 51.09 | 10021.53 | 72.05 | 90.59 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/senet/seresnet18_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/senet/seresnet18-7b971c78-910v2.ckpt) | -Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode. - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ---------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------- | -| seresnet18 | 11.80 | 8 | 64 | 224x224 | O2 | 43s | 44.40 | 11531.53 | 71.81 | 90.49 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/senet/seresnet18_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/senet/seresnet18-7880643b.ckpt) | - ### Notes - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. diff --git a/mshub_res/assets/mindspore/2.5/shufflenet_v1_g3_05_imagenet2012.md b/mshub_res/assets/mindspore/2.5/shufflenet_v1_g3_05_imagenet2012.md index 56b3ec6aeb1eaf0b2d06892834809708470db540..e56e9c10e604237d0a0d485ce5e986ed96784f29 100644 --- a/mshub_res/assets/mindspore/2.5/shufflenet_v1_g3_05_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/shufflenet_v1_g3_05_imagenet2012.md @@ -75,12 +75,6 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. | ------------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------- | | shufflenet_v1_g3_05 | 0.73 | 8 | 64 | 224x224 | O2 | 191s | 47.77 | 10718.02 | 57.08 | 79.89 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/shufflenetv1/shufflenet_v1_0.5_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/shufflenet/shufflenetv1/shufflenet_v1_g3_05-56209ef3-910v2.ckpt) | -Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode. - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ------------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------ | -| shufflenet_v1_g3_05 | 0.73 | 8 | 64 | 224x224 | O2 | 169s | 40.62 | 12604.63 | 57.05 | 79.73 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/shufflenetv1/shufflenet_v1_0.5_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/shufflenet/shufflenetv1/shufflenet_v1_g3_05-42cfe109.ckpt) | - ### Notes - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. diff --git a/mshub_res/assets/mindspore/2.5/shufflenet_v2_x0_5_imagenet2012.md b/mshub_res/assets/mindspore/2.5/shufflenet_v2_x0_5_imagenet2012.md index 6fa6c9fab612a17adaca2e7f370921f0ea05960f..7fe622db215a8f5c4c561adb3013979165bd44ba 100644 --- a/mshub_res/assets/mindspore/2.5/shufflenet_v2_x0_5_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/shufflenet_v2_x0_5_imagenet2012.md @@ -82,12 +82,6 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. | ------------------ | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------- | | shufflenet_v2_x0_5 | 1.37 | 8 | 64 | 224x224 | O2 | 100s | 47.32 | 10819.95 | 60.65 | 82.26 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/shufflenetv2/shufflenet_v2_0.5_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/shufflenet/shufflenetv2/shufflenet_v2_x0_5-39d05bb6-910v2.ckpt) | -Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode. - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ------------------ | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------------------------- | -| shufflenet_v2_x0_5 | 1.37 | 8 | 64 | 224x224 | O2 | 62s | 41.87 | 12228.33 | 60.53 | 82.11 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/shufflenetv2/shufflenet_v2_0.5_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/shufflenet/shufflenetv2/shufflenet_v2_x0_5-8c841061.ckpt) | - ### Notes - All models are trained on ImageNet-1K training set and the top-1 accuracy is reported on the validatoin set. diff --git a/mshub_res/assets/mindspore/2.5/skresnet18_imagenet2012.md b/mshub_res/assets/mindspore/2.5/skresnet18_imagenet2012.md index 69610e50985fb3b1638878f500082d44fed63de8..ac229242e4bbf65aeca40322a48bf0c5f1543fa4 100644 --- a/mshub_res/assets/mindspore/2.5/skresnet18_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/skresnet18_imagenet2012.md @@ -80,12 +80,6 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. | ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ---------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------- | | skresnet18 | 11.97 | 8 | 64 | 224x224 | O2 | 134s | 49.83 | 10274.93 | 72.85 | 90.83 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/sknet/skresnet18_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/sknet/skresnet18-9d8b1afc-910v2.ckpt) | -Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode. - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ---------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------- | -| skresnet18 | 11.97 | 8 | 64 | 224x224 | O2 | 60s | 45.84 | 11169.28 | 73.09 | 91.20 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/sknet/skresnet18_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/sknet/skresnet18-868228e5.ckpt) | - ### Notes - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. diff --git a/mshub_res/assets/mindspore/2.5/squeezenet1_0_imagenet2012.md b/mshub_res/assets/mindspore/2.5/squeezenet1_0_imagenet2012.md index c5f4dd2d8837167fffef42d37ac00def3689b3ab..3096de64ae0b82af107c5889e39ab79678ff24ac 100644 --- a/mshub_res/assets/mindspore/2.5/squeezenet1_0_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/squeezenet1_0_imagenet2012.md @@ -77,12 +77,6 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. | ------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- | | squeezenet1_0 | 1.25 | 8 | 32 | 224x224 | O2 | 64s | 23.48 | 10902.90 | 58.75 | 80.76 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/squeezenet/squeezenet_1.0_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/squeezenet/squeezenet1_0-24010b28-910v2.ckpt) | -Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode. - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- | -| squeezenet1_0 | 1.25 | 8 | 32 | 224x224 | O2 | 45s | 22.36 | 11449.02 | 58.67 | 80.61 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/squeezenet/squeezenet_1.0_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/squeezenet/squeezenet1_0-eb911778.ckpt) | - ### Notes - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. diff --git a/mshub_res/assets/mindspore/2.5/swin_tiny_imagenet2012.md b/mshub_res/assets/mindspore/2.5/swin_tiny_imagenet2012.md index 8ea9a5161be1fde3b946184fb02c9a91568dd302..a8bf0cf542552ed22f31625d0f5d671f03790dac 100644 --- a/mshub_res/assets/mindspore/2.5/swin_tiny_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/swin_tiny_imagenet2012.md @@ -84,12 +84,6 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. | ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------- | | swin_tiny | 33.38 | 8 | 256 | 224x224 | O2 | 266s | 466.6 | 4389.20 | 80.90 | 94.90 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/swintransformer/swin_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/swin/swin_tiny-72b3c5e6-910v2.ckpt) | -Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode. - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------- | -| swin_tiny | 33.38 | 8 | 256 | 224x224 | O2 | 226s | 454.49 | 4506.15 | 80.82 | 94.80 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/swintransformer/swin_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/swin/swin_tiny-0ff2f96d.ckpt) | - ### Notes - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. diff --git a/mshub_res/assets/mindspore/2.5/swinv2_tiny_window8_imagenet2012.md b/mshub_res/assets/mindspore/2.5/swinv2_tiny_window8_imagenet2012.md index df39e6bc9202bd87da9acf0a7134e3ffe0c28ed1..9cfaadddfd2b4edf18151a5117e137fbf48bf6b4 100644 --- a/mshub_res/assets/mindspore/2.5/swinv2_tiny_window8_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/swinv2_tiny_window8_imagenet2012.md @@ -78,12 +78,6 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. | ------------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------- | | swinv2_tiny_window8 | 28.78 | 8 | 128 | 256x256 | O2 | 385s | 335.18 | 3055.07 | 81.38 | 95.46 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/swintransformerv2/swinv2_tiny_window8_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/swinv2/swinv2_tiny_window8-70c5e903-910v2.ckpt) | -Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode. - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ------------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------- | -| swinv2_tiny_window8 | 28.78 | 8 | 128 | 256x256 | O2 | 273s | 317.19 | 3228.35 | 81.42 | 95.43 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/swintransformerv2/swinv2_tiny_window8_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/swinv2/swinv2_tiny_window8-3ef8b787.ckpt) | - ### Notes - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. diff --git a/mshub_res/assets/mindspore/2.5/vgg13_imagenet2012.md b/mshub_res/assets/mindspore/2.5/vgg13_imagenet2012.md index 941351a76dfe3da71ba80499d8f68ee7e49d436d..602b93e2d65c14c339463bd0a1375aefd496283a 100644 --- a/mshub_res/assets/mindspore/2.5/vgg13_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/vgg13_imagenet2012.md @@ -81,13 +81,6 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. | vgg13 | 133.04 | 8 | 32 | 224x224 | O2 | 41s | 30.52 | 8387.94 | 72.81 | 91.02 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/vgg/vgg13_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/vgg/vgg13-7756f33c-910v2.ckpt) | | vgg19 | 143.66 | 8 | 32 | 224x224 | O2 | 53s | 39.17 | 6535.61 | 75.24 | 92.55 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/vgg/vgg19_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/vgg/vgg19-5104d1ea-910v2.ckpt) | -Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode. - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------- | -| vgg13 | 133.04 | 8 | 32 | 224x224 | O2 | 23s | 55.20 | 4637.68 | 72.87 | 91.02 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/vgg/vgg13_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/vgg/vgg13-da805e6e.ckpt) | -| vgg19 | 143.66 | 8 | 32 | 224x224 | O2 | 22s | 67.42 | 3797.09 | 75.21 | 92.56 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/vgg/vgg19_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/vgg/vgg19-bedee7b6.ckpt) | - ### Notes - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. diff --git a/mshub_res/assets/mindspore/2.5/vgg19_imagenet2012.md b/mshub_res/assets/mindspore/2.5/vgg19_imagenet2012.md index e775326e86fabc2d1baa97874d63aea13b6807ae..9167e2b4f7c445ba09f0fb4fedda76ee1cbb291d 100644 --- a/mshub_res/assets/mindspore/2.5/vgg19_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/vgg19_imagenet2012.md @@ -81,13 +81,6 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. | vgg13 | 133.04 | 8 | 32 | 224x224 | O2 | 41s | 30.52 | 8387.94 | 72.81 | 91.02 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/vgg/vgg13_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/vgg/vgg13-7756f33c-910v2.ckpt) | | vgg19 | 143.66 | 8 | 32 | 224x224 | O2 | 53s | 39.17 | 6535.61 | 75.24 | 92.55 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/vgg/vgg19_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/vgg/vgg19-5104d1ea-910v2.ckpt) | -Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode. - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------- | -| vgg13 | 133.04 | 8 | 32 | 224x224 | O2 | 23s | 55.20 | 4637.68 | 72.87 | 91.02 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/vgg/vgg13_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/vgg/vgg13-da805e6e.ckpt) | -| vgg19 | 143.66 | 8 | 32 | 224x224 | O2 | 22s | 67.42 | 3797.09 | 75.21 | 92.56 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/vgg/vgg19_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/vgg/vgg19-bedee7b6.ckpt) | - ### Notes - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. diff --git a/mshub_res/assets/mindspore/2.5/visformer_tiny_imagenet2012.md b/mshub_res/assets/mindspore/2.5/visformer_tiny_imagenet2012.md index af13fe23e6a80e2d4ac34c3e17476e79fcdf7a72..b93deb30887c1ac92f0e5607b9dfd1815608ff49 100644 --- a/mshub_res/assets/mindspore/2.5/visformer_tiny_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/visformer_tiny_imagenet2012.md @@ -74,12 +74,6 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. _coming soon_ -Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode. - -| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | -| -------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------- | -| visformer_tiny | 10.33 | 8 | 128 | 224x224 | O2 | 137s | 217.92 | 4698.97 | 78.28 | 94.15 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/visformer/visformer_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/visformer/visformer_tiny-daee0322.ckpt) | - ### Notes - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. diff --git a/mshub_res/assets/mindspore/2.5/vit_b32_224_imagenet2012.md b/mshub_res/assets/mindspore/2.5/vit_b32_224_imagenet2012.md index 9a2281431ed4eae964d67205f746870f3a664d84..43c67b40e9c9984aa2b7c80555e181f870f82ae4 100644 --- a/mshub_res/assets/mindspore/2.5/vit_b32_224_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/vit_b32_224_imagenet2012.md @@ -84,10 +84,6 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. _coming soon_ -Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode. - -_coming soon_ - ### Notes - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. diff --git a/mshub_res/assets/mindspore/2.5/vit_l32_224_imagenet2012.md b/mshub_res/assets/mindspore/2.5/vit_l32_224_imagenet2012.md index 7285219c990b15cfc66a0a935d630bec526a1925..4c4658750ca6673b4159fa247b558a1fc0402f65 100644 --- a/mshub_res/assets/mindspore/2.5/vit_l32_224_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/vit_l32_224_imagenet2012.md @@ -84,10 +84,6 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. _coming soon_ -Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode. - -_coming soon_ - ### Notes - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. diff --git a/mshub_res/assets/mindspore/2.5/xcit_tiny_12_p16_224_imagenet2012.md b/mshub_res/assets/mindspore/2.5/xcit_tiny_12_p16_224_imagenet2012.md index 2c587af1b9b365b87524467649f4a86c7ce7b31f..774681be94c9e8fcb83ebd6fe0ea8faf9b53e62c 100644 --- a/mshub_res/assets/mindspore/2.5/xcit_tiny_12_p16_224_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/xcit_tiny_12_p16_224_imagenet2012.md @@ -73,10 +73,6 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. _coming soon_ -Experiments are tested on ascend 910 with mindspore 2.5.0 graph mode. - -_coming soon_ - ### Notes - top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K. diff --git a/mshub_res/assets/mindspore/2.5/yolov3_darknet53_coco2017.md b/mshub_res/assets/mindspore/2.5/yolov3_darknet53_coco2017.md index c44d8ae5169233c1a6a263d4adaf4a8eff73c220..8a4fe35ab588e88c90dcf2e05a58daa20f4dc40b 100644 --- a/mshub_res/assets/mindspore/2.5/yolov3_darknet53_coco2017.md +++ b/mshub_res/assets/mindspore/2.5/yolov3_darknet53_coco2017.md @@ -119,12 +119,6 @@ Experiments are tested on Ascend 910\* with mindspore 2.5.0 graph mode. | :--------: | :---: | :--------: | :--------: | :-------: | :-----------: | :-----: | :----: | :---: | :-------------------: | :------------------------------------------------------------------------------------------------------------------------: | | YOLOv3 | 8 | 16 | 640x640 | O2 | 274.32s | 383.68 | 333.61 | 46.6% | [yaml](./yolov3.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindyolo/yolov3/yolov3-darknet53_300e_mAP455-81895f09-910v2.ckpt) | -Experiments are tested on Ascend 910 with mindspore 2.5.0 graph mode. - -| model name | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | map | recipe | weight | -| :--------: | :---: | :--------: | :--------: | :-------: | :-----------: | :-----: | :----: | :---: | :-------------------: | :----------------------------------------------------------------------------------------------------------: | -| YOLOv3 | 8 | 16 | 640x640 | O2 | 160.80s | 409.66 | 312.45 | 45.5% | [yaml](./yolov3.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov3/yolov3-darknet53_300e_mAP455-adfb27af.ckpt) | -
### Notes diff --git a/mshub_res/assets/mindspore/2.5/yolov4_cspdarknet53_coco2017.md b/mshub_res/assets/mindspore/2.5/yolov4_cspdarknet53_coco2017.md index 8018f4942bab6ff3ababa717cbf64ced00cd3f9b..4a874014522ea0ca70d034317b2b71523682675f 100644 --- a/mshub_res/assets/mindspore/2.5/yolov4_cspdarknet53_coco2017.md +++ b/mshub_res/assets/mindspore/2.5/yolov4_cspdarknet53_coco2017.md @@ -141,13 +141,6 @@ Experiments are tested on Ascend 910\* with mindspore 2.5.0 graph mode. | :--------: | :----------: | :---: | :--------: | :--------: | :-------: | :-----------: | :-----: | :----: | :---: | :-------------------: | :---------------------------------------------------------------------------------------------------------------------------: | | YOLOv4 | CSPDarknet53 | 8 | 16 | 608x608 | O2 | 467.47s | 308.43 | 415.01 | 46.1% | [yaml](./yolov4.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindyolo/yolov4/yolov4-cspdarknet53_320e_map454-64b8506f-910v2.ckpt) | -Experiments are tested on Ascend 910 with mindspore 2.5.0 graph mode. - -| model name | backbone | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | map | recipe | weight | -| :--------: | :----------------: | :---: | :--------: | :--------: | :-------: | :-----------: | :-----: | :----: | :---: | :------------------------: | :------------------------------------------------------------------------------------------------------------------: | -| YOLOv4 | CSPDarknet53 | 8 | 16 | 608x608 | O2 | 188.52s | 505.98 | 252.97 | 45.4% | [yaml](./yolov4.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov4/yolov4-cspdarknet53_320e_map454-50172f93.ckpt) | -| YOLOv4 | CSPDarknet53(silu) | 8 | 16 | 608x608 | O2 | 274.18s | 443.21 | 288.80 | 45.8% | [yaml](./yolov4-silu.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov4/yolov4-cspdarknet53_silu_320e_map458-bdfc3205.ckpt) | -
### Notes diff --git a/mshub_res/assets/mindspore/2.5/yolov5n_coco2017.md b/mshub_res/assets/mindspore/2.5/yolov5n_coco2017.md index da2c0aa91be571d42e69e2433d2de3f2b14350dd..54d392561e6c908aa97e396e10a995b1964cbd57 100644 --- a/mshub_res/assets/mindspore/2.5/yolov5n_coco2017.md +++ b/mshub_res/assets/mindspore/2.5/yolov5n_coco2017.md @@ -119,16 +119,6 @@ Experiments are tested on Ascend 910\* with mindspore 2.5.0 graph mode. | YOLOv5 | L6 | 8 | 16 | 1280x1280 | O2 | 800.34s | 894.65 | 143.07 | 53.6% | [yaml](./yolov5l6.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindyolo/yolov5/yolov5l6_300e_mAP536-617a1cc1.ckpt) | | YOLOv5 | X6 | 8 | 8 | 1280x1280 | O2 | 995.73s | 864.43 | 74.04 | 54.5% | [yaml](./yolov5x6.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindyolo/yolov5/yolov5x6_300e_mAP545-81ebdca9.ckpt) | -Experiments are tested on Ascend 910 with mindspore 2.5.0 graph mode. - -| model name | scale | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | map | recipe | weight | -| :--------: | :---: | :---: | :--------: | :--------: | :-------: | :-----------: | :-----: | :----: | :---: | :--------------------: | :-------------------------------------------------------------------------------------------------: | -| YOLOv5 | N | 8 | 32 | 640x640 | O2 | 233.25s | 650.57 | 393.50 | 27.3% | [yaml](./yolov5n.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov5/yolov5n_300e_mAP273-9b16bd7b.ckpt) | -| YOLOv5 | S | 8 | 32 | 640x640 | O2 | 166.00s | 650.14 | 393.76 | 37.6% | [yaml](./yolov5s.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov5/yolov5s_300e_mAP376-860bcf3b.ckpt) | -| YOLOv5 | M | 8 | 32 | 640x640 | O2 | 256.51s | 712.31 | 359.39 | 44.9% | [yaml](./yolov5m.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov5/yolov5m_300e_mAP449-e7bbf695.ckpt) | -| YOLOv5 | L | 8 | 32 | 640x640 | O2 | 274.15s | 723.35 | 353.91 | 48.5% | [yaml](./yolov5l.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov5/yolov5l_300e_mAP485-a28bce73.ckpt) | -| YOLOv5 | X | 8 | 16 | 640x640 | O2 | 436.18s | 569.96 | 224.58 | 50.5% | [yaml](./yolov5x.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov5/yolov5x_300e_mAP505-97d36ddc.ckpt) | -
### Notes diff --git a/mshub_res/assets/mindspore/2.5/yolov5s_coco2017.md b/mshub_res/assets/mindspore/2.5/yolov5s_coco2017.md index 1ae8dfe795cc8808a4563ac438704a8e48462c46..03d9a79373051519db1835261d32addf82c543e6 100644 --- a/mshub_res/assets/mindspore/2.5/yolov5s_coco2017.md +++ b/mshub_res/assets/mindspore/2.5/yolov5s_coco2017.md @@ -119,16 +119,6 @@ Experiments are tested on Ascend 910\* with mindspore 2.5.0 graph mode. | YOLOv5 | L6 | 8 | 16 | 1280x1280 | O2 | 800.34s | 894.65 | 143.07 | 53.6% | [yaml](./yolov5l6.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindyolo/yolov5/yolov5l6_300e_mAP536-617a1cc1.ckpt) | | YOLOv5 | X6 | 8 | 8 | 1280x1280 | O2 | 995.73s | 864.43 | 74.04 | 54.5% | [yaml](./yolov5x6.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindyolo/yolov5/yolov5x6_300e_mAP545-81ebdca9.ckpt) | -Experiments are tested on Ascend 910 with mindspore 2.5.0 graph mode. - -| model name | scale | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | map | recipe | weight | -| :--------: | :---: | :---: | :--------: | :--------: | :-------: | :-----------: | :-----: | :----: | :---: | :--------------------: | :-------------------------------------------------------------------------------------------------: | -| YOLOv5 | N | 8 | 32 | 640x640 | O2 | 233.25s | 650.57 | 393.50 | 27.3% | [yaml](./yolov5n.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov5/yolov5n_300e_mAP273-9b16bd7b.ckpt) | -| YOLOv5 | S | 8 | 32 | 640x640 | O2 | 166.00s | 650.14 | 393.76 | 37.6% | [yaml](./yolov5s.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov5/yolov5s_300e_mAP376-860bcf3b.ckpt) | -| YOLOv5 | M | 8 | 32 | 640x640 | O2 | 256.51s | 712.31 | 359.39 | 44.9% | [yaml](./yolov5m.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov5/yolov5m_300e_mAP449-e7bbf695.ckpt) | -| YOLOv5 | L | 8 | 32 | 640x640 | O2 | 274.15s | 723.35 | 353.91 | 48.5% | [yaml](./yolov5l.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov5/yolov5l_300e_mAP485-a28bce73.ckpt) | -| YOLOv5 | X | 8 | 16 | 640x640 | O2 | 436.18s | 569.96 | 224.58 | 50.5% | [yaml](./yolov5x.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov5/yolov5x_300e_mAP505-97d36ddc.ckpt) | -
### Notes diff --git a/mshub_res/assets/mindspore/2.5/yolov7_tiny_coco2017.md b/mshub_res/assets/mindspore/2.5/yolov7_tiny_coco2017.md index 9ebb0cff9ac50e8b613152c4921669f1a8168d8b..01d63f31cc8a0d92e7ea742999f79aba686ad43e 100644 --- a/mshub_res/assets/mindspore/2.5/yolov7_tiny_coco2017.md +++ b/mshub_res/assets/mindspore/2.5/yolov7_tiny_coco2017.md @@ -109,14 +109,6 @@ Experiments are tested on Ascend 910\* with mindspore 2.5.0 graph mode. | :--------: | :---: | :---: | :--------: | :--------: | :-------: | :-----------: | :-----: | :----: | :---: | :------------------------: | :-------------------------------------------------------------------------------------------------------------------: | | YOLOv7 | Tiny | 8 | 16 | 640x640 | O2 | 363.74s | 352.92 | 362.69 | 37.5% | [yaml](./yolov7-tiny.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindyolo/yolov7/yolov7-tiny_300e_mAP375-1d2ddf4b-910v2.ckpt) | -Experiments are tested on Ascend 910 with mindspore 2.5.0 graph mode. - -| model name | scale | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | map | recipe | weight | -| :--------: | :---: | :---: | :--------: | :--------: | :-------: | :-----------: | :-----: | :----: | :---: | :------------------------: | :-----------------------------------------------------------------------------------------------------: | -| YOLOv7 | Tiny | 8 | 16 | 640x640 | O2 | 232.63s | 472.37 | 270.97 | 37.5% | [yaml](./yolov7-tiny.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov7/yolov7-tiny_300e_mAP375-d8972c94.ckpt) | -| YOLOv7 | L | 8 | 16 | 640x640 | O2 | 290.93s | 678.07 | 188.77 | 50.8% | [yaml](./yolov7.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov7/yolov7_300e_mAP508-734ac919.ckpt) | -| YOLOv7 | X | 8 | 12 | 640x640 | O2 | 404.77s | 636.36 | 150.86 | 52.4% | [yaml](./yolov7-x.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov7/yolov7-x_300e_mAP524-e2f58741.ckpt) | -
### Notes diff --git a/mshub_res/assets/mindspore/2.5/yolov8n_coco2017.md b/mshub_res/assets/mindspore/2.5/yolov8n_coco2017.md index 3a03e54973c183772dbcf05869b97debeeaf6d77..5408a9073043c8ffe27ad7a752f377cdbb06ee54 100644 --- a/mshub_res/assets/mindspore/2.5/yolov8n_coco2017.md +++ b/mshub_res/assets/mindspore/2.5/yolov8n_coco2017.md @@ -110,28 +110,12 @@ Experiments are tested on Ascend 910\* with mindspore 2.5.0 graph mode. | YOLOv8 | N | 8 | 16 | 640x640 | O2 | 145.89s | 252.79 | 506.35 | 37.3% | [yaml](./yolov8n.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindyolo/yolov8/yolov8-n_500e_mAP372-0e737186-910v2.ckpt) | | YOLOv8 | S | 8 | 16 | 640x640 | O2 | 172.22s | 251.30 | 509.35 | 44.7% | [yaml](./yolov8s.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindyolo/yolov8/yolov8-s_500e_mAP446-fae4983f-910v2.ckpt) | -Experiments are tested on Ascend 910 with mindspore 2.5.0 graph mode. - -| model name | scale | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | map | recipe | weight | -| :--------: | :---: | :---: | :--------: | :--------: | :-------: | :-----------: | :-----: | :----: | :---: | :--------------------: | :--------------------------------------------------------------------------------------------------: | -| YOLOv8 | N | 8 | 16 | 640x640 | O2 | 195.63s | 265.13 | 482.78 | 37.2% | [yaml](./yolov8n.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov8/yolov8-n_500e_mAP372-cc07f5bd.ckpt) | -| YOLOv8 | S | 8 | 16 | 640x640 | O2 | 115.60s | 292.68 | 437.34 | 44.6% | [yaml](./yolov8s.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov8/yolov8-s_500e_mAP446-3086f0c9.ckpt) | -| YOLOv8 | M | 8 | 16 | 640x640 | O2 | 185.25s | 383.72 | 333.58 | 50.5% | [yaml](./yolov8m.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov8/yolov8-m_500e_mAP505-8ff7a728.ckpt) | -| YOLOv8 | L | 8 | 16 | 640x640 | O2 | 175.08s | 429.02 | 298.35 | 52.8% | [yaml](./yolov8l.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov8/yolov8-l_500e_mAP528-6e96d6bb.ckpt) | -| YOLOv8 | X | 8 | 16 | 640x640 | O2 | 183.68s | 521.97 | 245.22 | 53.7% | [yaml](./yolov8x.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov8/yolov8-x_500e_mAP537-b958e1c7.ckpt) | - ### Segmentation Experiments are tested on Ascend 910 with mindspore 2.5.0 graph mode. _coming soon_ -Experiments are tested on Ascend 910 with mindspore 2.5.0 graph mode. - -| model Name | scale | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | map | mask map | recipe | weight | -| :--------: | :---: | :---: | :--------: | :--------: | :-------: | :-----------: | :-----: | :----: | :---: | :------: | :----------------------------: | :------------------------------------------------------------------------------------------------------------: | -| YOLOv8-seg | X | 8 | 16 | 640x640 | O2 | 183.68s | 641.25 | 199.61 | 52.5% | 42.9% | [yaml](./seg/yolov8x-seg.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov8/yolov8-x-seg_300e_mAP_mask_429-b4920557.ckpt) | - ### Notes - map: Accuracy reported on the validation set. diff --git a/mshub_res/assets/mindspore/2.5/yolov8s_coco2017.md b/mshub_res/assets/mindspore/2.5/yolov8s_coco2017.md index ae8c905858b114db1f3afd9953b8e5e239f0f72f..8e0ad2eace1c38b358be27063178ace6dd1ee4a5 100644 --- a/mshub_res/assets/mindspore/2.5/yolov8s_coco2017.md +++ b/mshub_res/assets/mindspore/2.5/yolov8s_coco2017.md @@ -110,28 +110,12 @@ Experiments are tested on Ascend 910\* with mindspore 2.5.0 graph mode. | YOLOv8 | N | 8 | 16 | 640x640 | O2 | 145.89s | 252.79 | 506.35 | 37.3% | [yaml](./yolov8n.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindyolo/yolov8/yolov8-n_500e_mAP372-0e737186-910v2.ckpt) | | YOLOv8 | S | 8 | 16 | 640x640 | O2 | 172.22s | 251.30 | 509.35 | 44.7% | [yaml](./yolov8s.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindyolo/yolov8/yolov8-s_500e_mAP446-fae4983f-910v2.ckpt) | -Experiments are tested on Ascend 910 with mindspore 2.5.0 graph mode. - -| model name | scale | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | map | recipe | weight | -| :--------: | :---: | :---: | :--------: | :--------: | :-------: | :-----------: | :-----: | :----: | :---: | :--------------------: | :--------------------------------------------------------------------------------------------------: | -| YOLOv8 | N | 8 | 16 | 640x640 | O2 | 195.63s | 265.13 | 482.78 | 37.2% | [yaml](./yolov8n.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov8/yolov8-n_500e_mAP372-cc07f5bd.ckpt) | -| YOLOv8 | S | 8 | 16 | 640x640 | O2 | 115.60s | 292.68 | 437.34 | 44.6% | [yaml](./yolov8s.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov8/yolov8-s_500e_mAP446-3086f0c9.ckpt) | -| YOLOv8 | M | 8 | 16 | 640x640 | O2 | 185.25s | 383.72 | 333.58 | 50.5% | [yaml](./yolov8m.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov8/yolov8-m_500e_mAP505-8ff7a728.ckpt) | -| YOLOv8 | L | 8 | 16 | 640x640 | O2 | 175.08s | 429.02 | 298.35 | 52.8% | [yaml](./yolov8l.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov8/yolov8-l_500e_mAP528-6e96d6bb.ckpt) | -| YOLOv8 | X | 8 | 16 | 640x640 | O2 | 183.68s | 521.97 | 245.22 | 53.7% | [yaml](./yolov8x.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov8/yolov8-x_500e_mAP537-b958e1c7.ckpt) | - ### Segmentation Experiments are tested on Ascend 910 with mindspore 2.5.0 graph mode. _coming soon_ -Experiments are tested on Ascend 910 with mindspore 2.5.0 graph mode. - -| model Name | scale | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | map | mask map | recipe | weight | -| :--------: | :---: | :---: | :--------: | :--------: | :-------: | :-----------: | :-----: | :----: | :---: | :------: | :----------------------------: | :------------------------------------------------------------------------------------------------------------: | -| YOLOv8-seg | X | 8 | 16 | 640x640 | O2 | 183.68s | 641.25 | 199.61 | 52.5% | 42.9% | [yaml](./seg/yolov8x-seg.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolov8/yolov8-x-seg_300e_mAP_mask_429-b4920557.ckpt) | - ### Notes - map: Accuracy reported on the validation set. diff --git a/mshub_res/assets/mindspore/2.5/yolox_s_coco2017.md b/mshub_res/assets/mindspore/2.5/yolox_s_coco2017.md index ba412243ecaf08d422d1643f69f6649c8355baa8..5a326454769686ee34db0b9329a9d3450686b6f8 100644 --- a/mshub_res/assets/mindspore/2.5/yolox_s_coco2017.md +++ b/mshub_res/assets/mindspore/2.5/yolox_s_coco2017.md @@ -107,18 +107,6 @@ Experiments are tested on Ascend 910\* with mindspore 2.5.0 graph mode. | :--------: | :---: | :---: | :--------: | :--------: | :-------: | :-----------: | :-----: | :----: | :---: | :--------------------: | :--------------------------------------------------------------------------------------------------------------: | | YOLOX | S | 8 | 8 | 640x640 | O2 | 299.01s | 177.65 | 360.26 | 41.0% | [yaml](./yolox-s.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindyolo/yolox/yolox-s_300e_map407-cebd0183-910v2.ckpt) | -Experiments are tested on Ascend 910 with mindspore 2.5.0 graph mode. - -| model name | scale | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | map | recipe | weight | -| :--------: | :-------: | :---: | :--------: | :--------: | :-------: | :-----------: | :-----: | :----: | :---: | :----------------------------: | :--------------------------------------------------------------------------------------------------------: | -| YOLOX | N | 8 | 8 | 416x416 | O2 | 202.49s | 138.84 | 460.96 | 24.1% | [yaml](./yolox-nano.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolox/yolox-n_300e_map241-ec9815e3.ckpt) | -| YOLOX | Tiny | 8 | 8 | 416x416 | O2 | 169.71s | 126.85 | 504.53 | 33.3% | [yaml](./yolox-tiny.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolox/yolox-tiny_300e_map333-e5ae3a2e.ckpt) | -| YOLOX | S | 8 | 8 | 640x640 | O2 | 202.46s | 243.99 | 262.31 | 40.7% | [yaml](./yolox-s.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolox/yolox-s_300e_map407-0983e07f.ckpt) | -| YOLOX | M | 8 | 8 | 640x640 | O2 | 212.78s | 267.68 | 239.09 | 46.7% | [yaml](./yolox-m.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolox/yolox-m_300e_map467-1db321ee.ckpt) | -| YOLOX | L | 8 | 8 | 640x640 | O2 | 262.52s | 316.78 | 202.03 | 49.2% | [yaml](./yolox-l.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolox/yolox-l_300e_map492-52a4ab80.ckpt) | -| YOLOX | X | 8 | 8 | 640x640 | O2 | 341.33s | 415.67 | 153.97 | 51.6% | [yaml](./yolox-x.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolox/yolox-x_300e_map516-52216d90.ckpt) | -| YOLOX | Darknet53 | 8 | 8 | 640x640 | O2 | 198.15s | 407.53 | 157.04 | 47.7% | [yaml](./yolox-darknet53.yaml) | [weights](https://download.mindspore.cn/toolkits/mindyolo/yolox/yolox-darknet53_300e_map477-b5fcaba9.ckpt) | -
### Notes diff --git a/tests/ut/test_validator_md.py b/tests/ut/test_validator_md.py index ece2d200a7bfae8eb60debf69f53099debd2f05c..5d8cd270c319251a55c79e7af7f965e0e1af44df 100644 --- a/tests/ut/test_validator_md.py +++ b/tests/ut/test_validator_md.py @@ -44,5 +44,6 @@ def test_validate_md(): current_dir = os.path.dirname(os.path.abspath(__file__)) asset_dir = os.path.join(current_dir, '../../mshub_res/assets/mindspore/2.5') mds = _find_md(os.path.abspath(asset_dir), 1) - for md in random.choices(mds, k=1): - CellInfo(md) + if mds is None: + for md in random.choices(mds, k=1): + CellInfo(md)