From 6dddb06b5db2d098636797844aa625f1fa46aff3 Mon Sep 17 00:00:00 2001 From: liuchuting Date: Fri, 18 Apr 2025 10:27:43 +0800 Subject: [PATCH] update performance info --- mshub_res/assets/mindspore/2.5/bit_resnet50_imagenet2012.md | 6 ++++-- mshub_res/assets/mindspore/2.5/coat_tiny_imagenet2012.md | 6 ++++-- mshub_res/assets/mindspore/2.5/convit_tiny_imagenet2012.md | 2 +- .../assets/mindspore/2.5/convnext_tiny_imagenet2012.md | 2 +- .../assets/mindspore/2.5/convnextv2_tiny_imagenet2012.md | 2 +- mshub_res/assets/mindspore/2.5/crnn_vgg7_lmdbd.md | 4 ++-- mshub_res/assets/mindspore/2.5/crossvit_9_imagenet2012.md | 2 +- mshub_res/assets/mindspore/2.5/densenet121_imagenet2012.md | 2 +- .../assets/mindspore/2.5/edgenext_xx_small_imagenet2012.md | 2 +- .../assets/mindspore/2.5/efficientnet_b0_imagenet2012.md | 2 +- mshub_res/assets/mindspore/2.5/ghostnet_050_imagenet2012.md | 6 ++++-- mshub_res/assets/mindspore/2.5/googlenet_imagenet2012.md | 2 +- mshub_res/assets/mindspore/2.5/hrnet_w32_imagenet2012.md | 2 +- mshub_res/assets/mindspore/2.5/inception_v3_imagenet2012.md | 2 +- mshub_res/assets/mindspore/2.5/inception_v4_imagenet2012.md | 2 +- mshub_res/assets/mindspore/2.5/mixnet_s_imagenet2012.md | 2 +- mshub_res/assets/mindspore/2.5/mnasnet_075_imagenet2012.md | 2 +- .../assets/mindspore/2.5/mobilenet_v1_025_imagenet2012.md | 2 +- .../assets/mindspore/2.5/mobilenet_v2_075_imagenet2012.md | 2 +- .../mindspore/2.5/mobilenet_v3_large_100_imagenet2012.md | 2 +- .../mindspore/2.5/mobilenet_v3_small_100_imagenet2012.md | 2 +- .../assets/mindspore/2.5/mobilevit_xx_small_imagenet2012.md | 2 +- .../assets/mindspore/2.5/nasnet_a_4x1056_imagenet2012.md | 2 +- mshub_res/assets/mindspore/2.5/pit_ti_imagenet2012.md | 2 +- .../assets/mindspore/2.5/poolformer_s12_imagenet2012.md | 2 +- mshub_res/assets/mindspore/2.5/pvt_tiny_imagenet2012.md | 2 +- mshub_res/assets/mindspore/2.5/pvt_v2_b0_imagenet2012.md | 2 +- .../assets/mindspore/2.5/regnet_x_800mf_imagenet2012.md | 2 +- mshub_res/assets/mindspore/2.5/repvgg_a0_imagenet2012.md | 2 +- mshub_res/assets/mindspore/2.5/repvgg_a1_imagenet2012.md | 2 +- mshub_res/assets/mindspore/2.5/res2net50_imagenet2012.md | 2 +- mshub_res/assets/mindspore/2.5/resnet50_imagenet2012.md | 2 +- mshub_res/assets/mindspore/2.5/resnetv2_50_imagenet2012.md | 2 +- .../assets/mindspore/2.5/resnext50_32x4d_imagenet2012.md | 2 +- mshub_res/assets/mindspore/2.5/rexnet_09_imagenet2012.md | 6 ++++-- mshub_res/assets/mindspore/2.5/seresnet18_imagenet2012.md | 2 +- .../mindspore/2.5/shufflenet_v1_g3_05_imagenet2012.md | 2 +- .../assets/mindspore/2.5/shufflenet_v2_x0_5_imagenet2012.md | 2 +- mshub_res/assets/mindspore/2.5/skresnet18_imagenet2012.md | 2 +- .../assets/mindspore/2.5/squeezenet1_0_imagenet2012.md | 2 +- mshub_res/assets/mindspore/2.5/swin_tiny_imagenet2012.md | 2 +- .../mindspore/2.5/swinv2_tiny_window8_imagenet2012.md | 2 +- mshub_res/assets/mindspore/2.5/vgg13_imagenet2012.md | 2 +- mshub_res/assets/mindspore/2.5/vgg19_imagenet2012.md | 2 +- .../assets/mindspore/2.5/visformer_tiny_imagenet2012.md | 6 ++++-- mshub_res/assets/mindspore/2.5/vit_b32_224_imagenet2012.md | 6 ++++-- mshub_res/assets/mindspore/2.5/vit_l32_224_imagenet2012.md | 6 ++++-- .../mindspore/2.5/xcit_tiny_12_p16_224_imagenet2012.md | 6 ++++-- mshub_res/assets/mindspore/2.5/yolov3_darknet53_coco2017.md | 2 +- .../assets/mindspore/2.5/yolov4_cspdarknet53_coco2017.md | 2 +- mshub_res/assets/mindspore/2.5/yolov5n_coco2017.md | 2 +- mshub_res/assets/mindspore/2.5/yolov5s_coco2017.md | 2 +- mshub_res/assets/mindspore/2.5/yolov7_tiny_coco2017.md | 2 +- mshub_res/assets/mindspore/2.5/yolov8n_coco2017.md | 2 +- mshub_res/assets/mindspore/2.5/yolov8s_coco2017.md | 2 +- mshub_res/assets/mindspore/2.5/yolox_s_coco2017.md | 2 +- 56 files changed, 81 insertions(+), 65 deletions(-) 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 2036707..5d6fb22 100644 --- a/mshub_res/assets/mindspore/2.5/bit_resnet50_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/bit_resnet50_imagenet2012.md @@ -64,9 +64,11 @@ too low. 5) With BiT fine-tuning, good performance can be achieved even if there Our reproduced model performance on ImageNet-1K is reported as follows. -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines with mindspore 2.5.0 graph mode. -_coming soon_ +| 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 | 171s | 60.48 | 4232.80 | 76.72 | 93.25 | [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_best_v2.ckpt) | ### Notes 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 c639d42..14fcba9 100644 --- a/mshub_res/assets/mindspore/2.5/coat_tiny_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/coat_tiny_imagenet2012.md @@ -58,9 +58,11 @@ Co-Scale Conv-Attentional Image Transformer (CoaT) is a Transformer-based image Our reproduced model performance on ImageNet-1K is reported as follows. -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines with mindspore 2.5.0 graph mode. -_coming soon_ +| 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 | 644s | 373.00 | 686.33 | 79.27 | 94.29 | [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_tiny-dcca16b1-910v2.ckpt) | ### Notes 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 dc49ac8..67bb917 100644 --- a/mshub_res/assets/mindspore/2.5/convit_tiny_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/convit_tiny_imagenet2012.md @@ -72,7 +72,7 @@ while offering a much improved sample efficiency.[[1](#references)] Our reproduced model performance on ImageNet-1K is reported as follows. -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines 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 | | ----------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------- | 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 5fb468a..8307ec5 100644 --- a/mshub_res/assets/mindspore/2.5/convnext_tiny_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/convnext_tiny_imagenet2012.md @@ -70,7 +70,7 @@ simplicity and efficiency of standard ConvNets.[[1](#references)] Our reproduced model performance on ImageNet-1K is reported as follows. -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines 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 | | ------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------- | 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 01bf619..6e8548a 100644 --- a/mshub_res/assets/mindspore/2.5/convnextv2_tiny_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/convnextv2_tiny_imagenet2012.md @@ -69,7 +69,7 @@ benchmarks, including ImageNet classification, COCO detection, and ADE20K segmen Our reproduced model performance on ImageNet-1K is reported as follows. -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines 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 | | --------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | -------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------- | 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 ba20f04..d57482d 100644 --- a/mshub_res/assets/mindspore/2.5/crnn_vgg7_lmdbd.md +++ b/mshub_res/assets/mindspore/2.5/crnn_vgg7_lmdbd.md @@ -366,7 +366,7 @@ For detailed instruction of data preparation and yaml configuration, please refe ### General Purpose Chinese Models -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines with mindspore 2.5.0 graph mode. | **model name** | **backbone** | **cards** | **batch size** | **language** | **jit level** | **graph compile** | **ms/step** | **img/s** | **scene** | **web** | **document** | **recipe** | **weight** | | :------------: | :----------: | :-------: | :------------: | :----------: | :-----------: | :---------------: | :---------: | :-------: | :-------: | :-----: | :----------: | :-----------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | @@ -378,7 +378,7 @@ Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. #### Training Performance -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines with mindspore 2.5.0 graph mode. | **model name** | **backbone** | **train dataset** | **params(M)** | **cards** | **batch size** | **jit level** | **graph compile** | **ms/step** | **img/s** | **accuracy** | **recipe** | **weight** | | :------------: | :----------: | :---------------: | :-----------: | :-------: | :------------: | :-----------: | :---------------: | :---------: | :-------: | :----------: | :--------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | 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 cdf7f5c..2a38b80 100644 --- a/mshub_res/assets/mindspore/2.5/crossvit_9_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/crossvit_9_imagenet2012.md @@ -67,7 +67,7 @@ Fusion is achieved by an efficient cross-attention module, in which each transfo Our reproduced model performance on ImageNet-1K is reported as follows. -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines 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 | | ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------- | diff --git a/mshub_res/assets/mindspore/2.5/densenet121_imagenet2012.md b/mshub_res/assets/mindspore/2.5/densenet121_imagenet2012.md index e95ecce..0db7727 100644 --- a/mshub_res/assets/mindspore/2.5/densenet121_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/densenet121_imagenet2012.md @@ -77,7 +77,7 @@ propagation, encourage feature reuse, and substantially reduce the number of par Our reproduced model performance on ImageNet-1K is reported as follows. -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines 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 | | ----------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------- | 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 cdc2c77..51385ed 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 @@ -69,7 +69,7 @@ to implicitly increase the receptive field and encode multi-scale features.[[1]( Our reproduced model performance on ImageNet-1K is reported as follows. -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines 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 | | ----------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | -------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------- | 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 7bad397..25ed10f 100644 --- a/mshub_res/assets/mindspore/2.5/efficientnet_b0_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/efficientnet_b0_imagenet2012.md @@ -73,7 +73,7 @@ and resolution scaling could be found. EfficientNet could achieve better model p Our reproduced model performance on ImageNet-1K is reported as follows. -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines 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 | | --------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------- | 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 1383958..109ebd7 100644 --- a/mshub_res/assets/mindspore/2.5/ghostnet_050_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/ghostnet_050_imagenet2012.md @@ -74,9 +74,11 @@ dataset.[[1](#references)] Our reproduced model performance on ImageNet-1K is reported as follows. -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines with mindspore 2.5.0 graph mode. -_coming soon_ +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +|:---------------:|:---------:|:-----:|:------------:|:------------:|:-----------:|:---------------:|:---------:|:---------:|:----------:|:----------:|:--------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:| +| ghostnet_050 | 2.6 | 8 | 128 | 224x224 | O2 | 125s | 201.46 | 5082.89 | 65.93 | 86.65 | [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-ae7771cb-910v2.ckpt) | ### Notes diff --git a/mshub_res/assets/mindspore/2.5/googlenet_imagenet2012.md b/mshub_res/assets/mindspore/2.5/googlenet_imagenet2012.md index 51b08b6..a0bde55 100644 --- a/mshub_res/assets/mindspore/2.5/googlenet_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/googlenet_imagenet2012.md @@ -70,7 +70,7 @@ training results.[[1](#references)] Our reproduced model performance on ImageNet-1K is reported as follows. -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines 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 | | ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------- | 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 6a3cfb1..8f8c190 100644 --- a/mshub_res/assets/mindspore/2.5/hrnet_w32_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/hrnet_w32_imagenet2012.md @@ -71,7 +71,7 @@ High-resolution representations are essential for position-sensitive vision prob Our reproduced model performance on ImageNet-1K is reported as follows. -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines 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 | | ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------- | 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 f5c8dac..8c6c47c 100644 --- a/mshub_res/assets/mindspore/2.5/inception_v3_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/inception_v3_imagenet2012.md @@ -71,7 +71,7 @@ regularization and effectively reduces overfitting.[[1](#references)] Our reproduced model performance on ImageNet-1K is reported as follows. -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines 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 | | ------------ | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------- | 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 af99812..7956030 100644 --- a/mshub_res/assets/mindspore/2.5/inception_v4_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/inception_v4_imagenet2012.md @@ -68,7 +68,7 @@ performance with Inception-ResNet v2.[[1](#references)] Our reproduced model performance on ImageNet-1K is reported as follows. -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines 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 | | ------------ | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------- | 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 9d7d686..2a20d9b 100644 --- a/mshub_res/assets/mindspore/2.5/mixnet_s_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/mixnet_s_imagenet2012.md @@ -70,7 +70,7 @@ and efficiency for existing MobileNets on both ImageNet classification and COCO Our reproduced model performance on ImageNet-1K is reported as follows. -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines 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 | | ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------- | 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 33e9874..a0432d2 100644 --- a/mshub_res/assets/mindspore/2.5/mnasnet_075_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/mnasnet_075_imagenet2012.md @@ -65,7 +65,7 @@ Designing convolutional neural networks (CNN) for mobile devices is challenging Our reproduced model performance on ImageNet-1K is reported as follows. -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines 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 | | ----------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | -------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------- | 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 1cab677..609cdcf 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 @@ -65,7 +65,7 @@ Compared with the traditional convolutional neural network, MobileNetV1's parame Our reproduced model performance on ImageNet-1K is reported as follows. -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines 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 | | ---------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ----------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------- | 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 c0feea0..b16085f 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 @@ -67,7 +67,7 @@ The main innovation of the model is the proposal of a new layer module: The Inve Our reproduced model performance on ImageNet-1K is reported as follows. -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines 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 | | ---------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ----------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------- | 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 df869ce..48677dc 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 @@ -67,7 +67,7 @@ mobilenet-v3 offers two versions, mobilenet-v3 large and mobilenet-v3 small, for Our reproduced model performance on ImageNet-1K is reported as follows. -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines 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 | | ---------------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------- | 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 b8846d1..37105d5 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 @@ -67,7 +67,7 @@ mobilenet-v3 offers two versions, mobilenet-v3 large and mobilenet-v3 small, for Our reproduced model performance on ImageNet-1K is reported as follows. -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines 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 | | ---------------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------- | 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 2f14c08..6065d8e 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 @@ -65,7 +65,7 @@ MobileViT, a light-weight and general-purpose vision transformer for mobile devi Our reproduced model performance on ImageNet-1K is reported as follows. -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines 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 | | ------------------ | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------- | 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 1ee6aad..7b29872 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 @@ -73,7 +73,7 @@ compared with previous state-of-the-art methods on ImageNet-1K dataset.[[1](#ref Our reproduced model performance on ImageNet-1K is reported as follows. -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines 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 | | --------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------ | 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 2399e46..15a59b5 100644 --- a/mshub_res/assets/mindspore/2.5/pit_ti_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/pit_ti_imagenet2012.md @@ -66,7 +66,7 @@ PiT (Pooling-based Vision Transformer) is an improvement of Vision Transformer ( Our reproduced model performance on ImageNet-1K is reported as follows. -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines 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 | | ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- | 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 e436037..0a188d1 100644 --- a/mshub_res/assets/mindspore/2.5/poolformer_s12_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/poolformer_s12_imagenet2012.md @@ -64,7 +64,7 @@ Figure 2. (a) The overall framework of PoolFormer. (b) The architecture of PoolF Our reproduced model performance on ImageNet-1K is reported as follows. -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines 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 | | -------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------- | 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 3d5fcc2..f93c21f 100644 --- a/mshub_res/assets/mindspore/2.5/pvt_tiny_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/pvt_tiny_imagenet2012.md @@ -64,7 +64,7 @@ overhead.[[1](#references)] Our reproduced model performance on ImageNet-1K is reported as follows. -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines 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 | | ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------- | 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 d864155..6fa1fe8 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 @@ -69,7 +69,7 @@ segmentation.[[1](#references)] Our reproduced model performance on ImageNet-1K is reported as follows. -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines 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 | | ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------- | 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 b552f5c..33e8091 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 @@ -73,7 +73,7 @@ has a higher concentration of good models.[[1](#references)] Our reproduced model performance on ImageNet-1K is reported as follows. -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines 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 | | -------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | --------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------- | 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 3fdc899..1865aed 100644 --- a/mshub_res/assets/mindspore/2.5/repvgg_a0_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/repvgg_a0_imagenet2012.md @@ -75,7 +75,7 @@ previous methods.[[1](#references)] Our reproduced model performance on ImageNet-1K is reported as follows. -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines 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 | | ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ---------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------- | 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 2349f6e..ac4b821 100644 --- a/mshub_res/assets/mindspore/2.5/repvgg_a1_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/repvgg_a1_imagenet2012.md @@ -75,7 +75,7 @@ previous methods.[[1](#references)] Our reproduced model performance on ImageNet-1K is reported as follows. -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines 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 | | ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ---------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------- | diff --git a/mshub_res/assets/mindspore/2.5/res2net50_imagenet2012.md b/mshub_res/assets/mindspore/2.5/res2net50_imagenet2012.md index cd595c1..38d0613 100644 --- a/mshub_res/assets/mindspore/2.5/res2net50_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/res2net50_imagenet2012.md @@ -70,7 +70,7 @@ state-of-the-art baseline methods such as ResNet-50, DLA-60 and etc. Our reproduced model performance on ImageNet-1K is reported as follows. -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines 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 | | ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------ | diff --git a/mshub_res/assets/mindspore/2.5/resnet50_imagenet2012.md b/mshub_res/assets/mindspore/2.5/resnet50_imagenet2012.md index 753ad39..af52553 100644 --- a/mshub_res/assets/mindspore/2.5/resnet50_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/resnet50_imagenet2012.md @@ -68,7 +68,7 @@ networks are easier to optimize, and can gain accuracy from considerably increas Our reproduced model performance on ImageNet-1K is reported as follows. -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines 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 | | ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ---------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------- | 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 40647f5..26ac3f0 100644 --- a/mshub_res/assets/mindspore/2.5/resnetv2_50_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/resnetv2_50_imagenet2012.md @@ -67,7 +67,7 @@ to any other block, when using identity mappings as the skip connections and aft Our reproduced model performance on ImageNet-1K is reported as follows. -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines 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 | | ----------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | -------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------- | 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 59b2bbb..9373d55 100644 --- a/mshub_res/assets/mindspore/2.5/resnext50_32x4d_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/resnext50_32x4d_imagenet2012.md @@ -71,7 +71,7 @@ accuracy.[[1](#references)] Our reproduced model performance on ImageNet-1K is reported as follows. -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines 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 | | --------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ----------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------ | 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 6f5d0f3..1b61823 100644 --- a/mshub_res/assets/mindspore/2.5/rexnet_09_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/rexnet_09_imagenet2012.md @@ -62,9 +62,11 @@ detection, instance segmentation, and fine-grained classifications. Our reproduced model performance on ImageNet-1K is reported as follows. -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines with mindspore 2.5.0 graph mode. -_coming soon_ +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +|:----------:|:---------:|:-----:|:------------:|:------------:|:-----------:|:---------------:|:---------:|:---------:|:----------:|:----------:|:--------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:| +| rexnet_x09 | 4.13 | 8 | 64 | 224x224 | O2 | 463s | 122.56 | 4177.55 | 76.15 | 92.89 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/rexnet/rexnet_x09_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/rexnet/rexnet_09-00223eb4-910v2.ckpt) | ### Notes diff --git a/mshub_res/assets/mindspore/2.5/seresnet18_imagenet2012.md b/mshub_res/assets/mindspore/2.5/seresnet18_imagenet2012.md index 1b5c01e..f16c90b 100644 --- a/mshub_res/assets/mindspore/2.5/seresnet18_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/seresnet18_imagenet2012.md @@ -70,7 +70,7 @@ additional computational cost.[[1](#references)] Our reproduced model performance on ImageNet-1K is reported as follows. -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines 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 | | ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ---------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------- | 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 e56e9c1..f8b2a7e 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 @@ -69,7 +69,7 @@ migrating a large trained model. Our reproduced model performance on ImageNet-1K is reported as follows. -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines 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 | | ------------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------- | 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 7fe622d..dbcf067 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 @@ -76,7 +76,7 @@ Therefore, based on these two principles, ShuffleNetV2 proposes four effective n Our reproduced model performance on ImageNet-1K is reported as follows. -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines 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 | | ------------------ | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------- | diff --git a/mshub_res/assets/mindspore/2.5/skresnet18_imagenet2012.md b/mshub_res/assets/mindspore/2.5/skresnet18_imagenet2012.md index ac22924..a5d0ab6 100644 --- a/mshub_res/assets/mindspore/2.5/skresnet18_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/skresnet18_imagenet2012.md @@ -74,7 +74,7 @@ multi-scale information from, e.g., 3×3, 5×5, 7×7 convolutional kernels insid Our reproduced model performance on ImageNet-1K is reported as follows. -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines 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 | | ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ---------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------- | 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 3096de6..79b7a52 100644 --- a/mshub_res/assets/mindspore/2.5/squeezenet1_0_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/squeezenet1_0_imagenet2012.md @@ -71,7 +71,7 @@ Middle: SqueezeNet with simple bypass; Right: SqueezeNet with complex bypass. Our reproduced model performance on ImageNet-1K is reported as follows. -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines 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 | | ------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | -------- | -------- | -------- | ------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- | 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 a8bf0cf..c4b4d34 100644 --- a/mshub_res/assets/mindspore/2.5/swin_tiny_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/swin_tiny_imagenet2012.md @@ -78,7 +78,7 @@ on ImageNet-1K dataset compared with ViT and ResNet.[[1](#references)] Our reproduced model performance on ImageNet-1K is reported as follows. -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines 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 | | ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------- | 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 9cfaadd..0e0d901 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 @@ -72,7 +72,7 @@ semantic segmentation, and Kinetics-400 video action classification.[[1](#refere Our reproduced model performance on ImageNet-1K is reported as follows. -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines 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 | | ------------------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | ------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------- | diff --git a/mshub_res/assets/mindspore/2.5/vgg13_imagenet2012.md b/mshub_res/assets/mindspore/2.5/vgg13_imagenet2012.md index 602b93e..32199a1 100644 --- a/mshub_res/assets/mindspore/2.5/vgg13_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/vgg13_imagenet2012.md @@ -74,7 +74,7 @@ methods such as GoogleLeNet and AlexNet on ImageNet-1K dataset.[[1](#references) Our reproduced model performance on ImageNet-1K is reported as follows. -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines 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 | | ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------- | diff --git a/mshub_res/assets/mindspore/2.5/vgg19_imagenet2012.md b/mshub_res/assets/mindspore/2.5/vgg19_imagenet2012.md index 9167e2b..41c6f98 100644 --- a/mshub_res/assets/mindspore/2.5/vgg19_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/vgg19_imagenet2012.md @@ -74,7 +74,7 @@ methods such as GoogleLeNet and AlexNet on ImageNet-1K dataset.[[1](#references) Our reproduced model performance on ImageNet-1K is reported as follows. -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines 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 | | ---------- | --------- | ----- | ---------- | ---------- | --------- | ------------- | ------- | ------- | -------- | -------- | --------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------- | 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 b93deb3..d38a0a6 100644 --- a/mshub_res/assets/mindspore/2.5/visformer_tiny_imagenet2012.md +++ b/mshub_res/assets/mindspore/2.5/visformer_tiny_imagenet2012.md @@ -70,9 +70,11 @@ BatchNorm to patch embedding modules as in CNNs. [[2](#references)] Our reproduced model performance on ImageNet-1K is reported as follows. -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines with mindspore 2.5.0 graph mode. -_coming soon_ +| 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 | 141s | 207.35 | 4938.51 | 74.93 | 92.55 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/visformer/visformer_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/visformer/visformer_tiny-df995ba4-910v2.ckpt) | ### Notes 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 43c67b4..ec398a2 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 @@ -80,9 +80,11 @@ fewer computational resources. [[2](#references)] Our reproduced model performance on ImageNet-1K is reported as follows. -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines with mindspore 2.5.0 graph mode. -_coming soon_ +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +|:----------:|:---------:|:-----:|:------------:|:------------:|:-----------:|:-------------:|:---------:|:-------:|:--------:|:--------:|:---------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:| +| vit_b32 | 88.22 | 8 | 512 | 224x224 | O2 | 192s | 815.23 | 5024.35 | 77.39 | 93.32 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/vit/vit_b32_224_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/vit/vit_b_32_224-a2165e72-910v2.ckpt) | ### Notes 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 4c46587..b933de4 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 @@ -80,9 +80,11 @@ fewer computational resources. [[2](#references)] Our reproduced model performance on ImageNet-1K is reported as follows. -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines with mindspore 2.5.0 graph mode. -_coming soon_ +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +|:----------:|:---------:|:-----:|:------------:|:------------:|:-----------:|:---------------:|:---------:|:---------:|:----------:|:----------:|:--------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:| +| vit_l32 | 306.54 | 8 | 256 | 224x224 | O2 | 225s | 425.36 | 4814.75 | 74.63 | 92.21 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/vit/vit_l32_224_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/vit/vit_l_32_224-e0039f16-910v2.ckpt) | ### Notes 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 774681b..d1c8f0c 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 @@ -69,9 +69,11 @@ transformers with the scalability of convolutional architectures. Our reproduced model performance on ImageNet-1K is reported as follows. -Experiments are tested on ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines with mindspore 2.5.0 graph mode. -_coming soon_ +| model name | params(M) | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | acc@top1 | acc@top5 | recipe | weight | +|:----------:|:---------:|:-----:|:------------:|:------------:|:-----------:|:---------------:|:---------:|:---------:|:----------:|:----------:|:--------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:| +| xcit_tiny | 6.72 | 8 | 128 | 224x224 | O2 | 329s | 233.86 | 4378.69 | 77.16 | 93.57 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/xcit/xcit_tiny_12_p16_224_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/xcit/xcit_tiny_12_p16_224-bd90776e-910v2.ckpt) | ### Notes 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 8a4fe35..3a97f17 100644 --- a/mshub_res/assets/mindspore/2.5/yolov3_darknet53_coco2017.md +++ b/mshub_res/assets/mindspore/2.5/yolov3_darknet53_coco2017.md @@ -113,7 +113,7 @@ python test.py --config ./configs/yolov3/yolov3.yaml --device_target Ascend --we ## Performance -Experiments are tested on Ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines with mindspore 2.5.0 graph mode. | model name | cards | batch size | resolution | jit level | graph compile | ms/step | img/s | map | recipe | weight | | :--------: | :---: | :--------: | :--------: | :-------: | :-----------: | :-----: | :----: | :---: | :-------------------: | :------------------------------------------------------------------------------------------------------------------------: | 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 4a87401..8510256 100644 --- a/mshub_res/assets/mindspore/2.5/yolov4_cspdarknet53_coco2017.md +++ b/mshub_res/assets/mindspore/2.5/yolov4_cspdarknet53_coco2017.md @@ -135,7 +135,7 @@ python test.py --config ./configs/yolov4/yolov4-silu.yaml --device_target Ascend ## Performance -Experiments are tested on Ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines 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 | | :--------: | :----------: | :---: | :--------: | :--------: | :-------: | :-----------: | :-----: | :----: | :---: | :-------------------: | :---------------------------------------------------------------------------------------------------------------------------: | diff --git a/mshub_res/assets/mindspore/2.5/yolov5n_coco2017.md b/mshub_res/assets/mindspore/2.5/yolov5n_coco2017.md index 54d3925..d98e47a 100644 --- a/mshub_res/assets/mindspore/2.5/yolov5n_coco2017.md +++ b/mshub_res/assets/mindspore/2.5/yolov5n_coco2017.md @@ -107,7 +107,7 @@ python test.py --config ./configs/yolov5/yolov5n6.yaml --device_target Ascend -- ## Performance -Experiments are tested on Ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines 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 | | :--------: | :---: | :---: | :--------: | :--------: | :-------: | :-----------: | :-----: | :----: | :---: | :---------------------: | :---------------------------------------------------------------------------------------------------------------: | diff --git a/mshub_res/assets/mindspore/2.5/yolov5s_coco2017.md b/mshub_res/assets/mindspore/2.5/yolov5s_coco2017.md index 03d9a79..ec77bbe 100644 --- a/mshub_res/assets/mindspore/2.5/yolov5s_coco2017.md +++ b/mshub_res/assets/mindspore/2.5/yolov5s_coco2017.md @@ -107,7 +107,7 @@ python test.py --config ./configs/yolov5/yolov5n6.yaml --device_target Ascend -- ## Performance -Experiments are tested on Ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines 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 | | :--------: | :---: | :---: | :--------: | :--------: | :-------: | :-----------: | :-----: | :----: | :---: | :---------------------: | :---------------------------------------------------------------------------------------------------------------: | 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 01d63f3..1fac36e 100644 --- a/mshub_res/assets/mindspore/2.5/yolov7_tiny_coco2017.md +++ b/mshub_res/assets/mindspore/2.5/yolov7_tiny_coco2017.md @@ -103,7 +103,7 @@ python test.py --config ./configs/yolov7/yolov7.yaml --device_target Ascend --we ## Performance -Experiments are tested on Ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines 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 | | :--------: | :---: | :---: | :--------: | :--------: | :-------: | :-----------: | :-----: | :----: | :---: | :------------------------: | :-------------------------------------------------------------------------------------------------------------------: | diff --git a/mshub_res/assets/mindspore/2.5/yolov8n_coco2017.md b/mshub_res/assets/mindspore/2.5/yolov8n_coco2017.md index 5408a90..2ec0c1a 100644 --- a/mshub_res/assets/mindspore/2.5/yolov8n_coco2017.md +++ b/mshub_res/assets/mindspore/2.5/yolov8n_coco2017.md @@ -103,7 +103,7 @@ python test.py --config ./configs/yolov8/yolov8n.yaml --device_target Ascend --w ### Detection -Experiments are tested on Ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines 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 | | :--------: | :---: | :---: | :--------: | :--------: | :-------: | :-----------: | :-----: | :----: | :---: | :--------------------: | :----------------------------------------------------------------------------------------------------------------: | diff --git a/mshub_res/assets/mindspore/2.5/yolov8s_coco2017.md b/mshub_res/assets/mindspore/2.5/yolov8s_coco2017.md index 8e0ad2e..2c70f9b 100644 --- a/mshub_res/assets/mindspore/2.5/yolov8s_coco2017.md +++ b/mshub_res/assets/mindspore/2.5/yolov8s_coco2017.md @@ -103,7 +103,7 @@ python test.py --config ./configs/yolov8/yolov8n.yaml --device_target Ascend --w ### Detection -Experiments are tested on Ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines 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 | | :--------: | :---: | :---: | :--------: | :--------: | :-------: | :-----------: | :-----: | :----: | :---: | :--------------------: | :----------------------------------------------------------------------------------------------------------------: | 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 5a32645..5fc1853 100644 --- a/mshub_res/assets/mindspore/2.5/yolox_s_coco2017.md +++ b/mshub_res/assets/mindspore/2.5/yolox_s_coco2017.md @@ -101,7 +101,7 @@ python test.py --config ./configs/yolox/yolox-s.yaml --device_target Ascend --we ## Performance -Experiments are tested on Ascend 910\* with mindspore 2.5.0 graph mode. +Experiments are tested on Ascend Atlas 800T A2 machines 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 | | :--------: | :---: | :---: | :--------: | :--------: | :-------: | :-----------: | :-----: | :----: | :---: | :--------------------: | :--------------------------------------------------------------------------------------------------------------: | -- Gitee