# TResNet
**Repository Path**: altriasjy31/TResNet
## Basic Information
- **Project Name**: TResNet
- **Description**: import from github for efficient downloading
https://github.com/Alibaba-MIIL/TResNet.git
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-07-03
- **Last Updated**: 2022-05-28
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
# TResNet: High Performance GPU-Dedicated Architecture
[](https://paperswithcode.com/sota/image-classification-on-cifar-10?p=tresnet-high-performance-gpu-dedicated)
[](https://paperswithcode.com/sota/image-classification-on-cifar-100?p=tresnet-high-performance-gpu-dedicated)
[](https://paperswithcode.com/sota/fine-grained-image-classification-on-stanford?p=imagenet-21k-pretraining-for-the-masses)
[](https://paperswithcode.com/sota/fine-grained-image-classification-on-oxford?p=tresnet-high-performance-gpu-dedicated)
[](https://paperswithcode.com/sota/multi-label-classification-on-ms-coco?p=imagenet-21k-pretraining-for-the-masses)
[](https://paperswithcode.com/sota/multi-label-classification-on-nus-wide?p=asymmetric-loss-for-multi-label)
[](https://paperswithcode.com/sota/multi-label-classification-on-pascal-voc-2007?p=imagenet-21k-pretraining-for-the-masses)
[paperV2](https://arxiv.org/pdf/2003.13630.pdf) |
[pretrained models](MODEL_ZOO.md)
Official PyTorch Implementation
> Tal Ridnik, Hussam Lawen, Asaf Noy, Itamar Friedman, Emanuel Ben Baruch, Gilad Sharir
> DAMO Academy, Alibaba Group
**Abstract**
> Many deep learning models, developed in recent years, reach higher
> ImageNet accuracy than ResNet50, with fewer or comparable FLOPS count.
> While FLOPs are often seen as a proxy for network efficiency, when
> measuring actual GPU training and inference throughput, vanilla
> ResNet50 is usually significantly faster than its recent competitors,
> offering better throughput-accuracy trade-off. In this work, we
> introduce a series of architecture modifications that aim to boost
> neural networks' accuracy, while retaining their GPU training and
> inference efficiency. We first demonstrate and discuss the bottlenecks
> induced by FLOPs-optimizations. We then suggest alternative designs
> that better utilize GPU structure and assets. Finally, we introduce a
> new family of GPU-dedicated models, called TResNet, which achieve
> better accuracy and efficiency than previous ConvNets. Using a TResNet
> model, with similar GPU throughput to ResNet50, we reach 80.7\%
> top-1 accuracy on ImageNet. Our TResNet models also transfer well and
> achieve state-of-the-art accuracy on competitive datasets such as
> Stanford cars (96.0\%), CIFAR-10 (99.0\%), CIFAR-100 (91.5\%) and
> Oxford-Flowers (99.1\%). They also perform well on multi-label classification and object detection tasks.
## 29/11/2021 Update - New article released, offering new classification head with state-of-the-art results
Checkout our new project, [Ml-Decoder](https://github.com/Alibaba-MIIL/ML_Decoder), which presents a unified classification head for multi-label, single-label and
zero-shot tasks. Backbones with ML-Decoder reach SOTA results, while also improving speed-accuracy tradeoff.
## 23/4/2021 Update - ImageNet21K Pretraining
In a new [article](https://github.com/Alibaba-MIIL/ImageNet21K) we released, we share pretrain weights for TResNet models from ImageNet21K training, that dramatically outperfrom standard pretraining.
TResNet-M model, for example, improves its ImageNet-1K score, from 80.7% to 83.1% !
This kind of improvement is consistently achieved on all downstream tasks.
## 28/8/2020: V2 of TResNet Article Released
## Sotabench Comparisons
Comparative results from
[sotabench benchamrk](https://sotabench.com/benchmarks/image-classification-on-imagenet#code),
demonstartaing that TReNset models give excellent speed-accuracy tradoff:
## 11/6/2020: V1 of TResNet Article Released
The main change - In addition to single label SOTA results, we also
added top results for multi-label classification and object detection
tasks, using TResNet. For example, we set a new SOTA record for MS-COCO
multi-label dataset, surpassing the previous top results by more than
2.5% mAP !
## 2/6/2020: CVPR-Kaggle competitions
We participated and won top places in two
major CVPR-Kaggle competitions:
* [2nd place](https://www.kaggle.com/c/herbarium-2020-fgvc7/discussion/154186)
in Herbarium 2020 competition, out of 153 teams.
* [7th place](https://www.kaggle.com/c/plant-pathology-2020-fgvc7/discussion/154086)
in Plant-Pathology 2020 competition, out of 1317 teams.
*TResNet* was a vital part of our solution for both competitions,
allowing us to work on high resolutions and reach top scores while
doing fast and efficient experiments.
## Main Article Results
#### TResNet Models
TResNet models accuracy and GPU throughput on ImageNet, compared to ResNet50. All measurements were done on Nvidia V100 GPU, with mixed precision. All models are trained on input resolution of 224.
| Models |
Top Training Speed
(img/sec) |
Top Inference Speed
(img/sec) |
Max Train Batch Size |
Top-1 Acc. |
| ResNet50 |
805 |
2830 |
288 |
79.0 |
| EfficientNetB1 |
440 |
2740 |
196 |
79.2 |
| TResNet-M |
730 |
2930 |
512 |
80.8 |
| TResNet-L |
345 |
1390 |
316 |
81.5 |
| TResNet-XL |
250 |
1060 |
240 |
82.0 |
#### Comparison To Other Networks
Comparison of ResNet50 to top modern networks, with similar top-1 ImageNet accuracy.
All measurements were done on Nvidia V100 GPU with mixed precision. For gaining optimal speeds, training and inference were measured on 90\% of maximal possible batch size.
Except TResNet-M, all the models' ImageNet scores were taken from the [public repository](https://github.com/rwightman/pytorch-image-models), which specialized in providing top implementations for modern networks. Except EfficientNet-B1, which has input resolution of 240, all other models have input resolution of 224.
| Model |
Top Training Speed
(img/sec) |
Top Inference Speed
(img/sec) |
Top-1 Acc. |
Flops[G] |
| ResNet50 |
805 |
2830 |
79.0 |
4.1 |
| ResNet50-D |
600 |
2670 |
79.3 |
4.4 |
| ResNeXt50 |
490 |
1940 |
79.4 |
4.3 |
| EfficientNetB1 |
440 |
2740 |
79.2 |
0.6 |
| SEResNeXt50 |
400 |
1770 |
79.9 |
4.3 |
| MixNet-L |
400 |
1400 |
79.0 |
0.5 |
| TResNet-M |
730 |
2930 |
80.8 |
5.5 |
#### Transfer Learning SotA Results
Comparison of TResNet to state-of-the-art models on transfer learning datasets (only ImageNet-based transfer learning results). Models inference speed is measured on a mixed precision V100 GPU. Since no official implementation of Gpipe was provided, its inference speed is unknown
|
Dataset
|
Model
|
Top-1
Acc.
|
Speed
img/sec
|
Input
|
|
CIFAR-10
|
Gpipe
|
99.0
|
-
|
480
|
|
TResNet-XL
|
99.0
|
1060
|
224
|
|
CIFAR-100
|
EfficientNet-B7
|
91.7
|
70
|
600
|
|
TResNet-XL
|
91.5
|
1060
|
224
|
|
Stanford Cars
|
EfficientNet-B7
|
94.7
|
70
|
600
|
|
TResNet-L
|
96.0
|
500
|
368
|
|
Oxford-Flowers
|
EfficientNet-B7
|
98.8
|
70
|
600
|
|
TResNet-L
|
99.1
|
500
|
368
|
## Reproduce Article Scores
We provide code for reproducing the validation top-1 score of TResNet
models on ImageNet. First, download pretrained models from
[here](MODEL_ZOO.md).
Then, run the infer.py script. For example, for tresnet_m (input size 224)
run:
```bash
python -m infer.py \
--val_dir=/path/to/imagenet_val_folder \
--model_path=/model/path/to/tresnet_m.pth \
--model_name=tresnet_m
--input_size=224
```
## TResNet Training
Due to IP limitations, we do not provide the exact training code that
was used to obtain the article results.
However, TResNet is now an integral part of the popular [rwightman /
pytorch-image-models](https://github.com/rwightman/pytorch-image-models)
repo. Using that repo, you can reach very similar results to the one
stated in the article.
For example, training tresnet_m on [rwightman /
pytorch-image-models](https://github.com/rwightman/pytorch-image-models) with
the command line:
```bash
python -u -m torch.distributed.launch --nproc_per_node=8 \
--nnodes=1 --node_rank=0 ./train.py /data/imagenet/ \
-b=190 --lr=0.6 --model-ema --aa=rand-m9-mstd0.5-inc1 \
--num-gpu=8 -j=16 --amp \
--model=tresnet_m --epochs=300 --mixup=0.2 \
--sched='cosine' --reprob=0.4 --remode=pixel
```
gave accuracy of 80.5%.
Also, during the merge request, we had interesting discussions and
insights regarding TResNet design. I am attaching a pdf version the
mentioned discussions. They can shed more light on TResNet design
considerations and directions for the future.
[TResNet discussion and insights](https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/tresnet/TResnet_discussion.pdf)
(taken with permission from
[here](https://github.com/rwightman/pytorch-image-models/issues/124))
## Tips For Working With Inplace-ABN
See
[INPLACE_ABN_TIPS](https://github.com/mrT23/TResNet/blob/master/INPLACE_ABN_TIPS.md).
## Citation
```
@misc{ridnik2020tresnet,
title={TResNet: High Performance GPU-Dedicated Architecture},
author={Tal Ridnik and Hussam Lawen and Asaf Noy and Itamar Friedman},
year={2020},
eprint={2003.13630},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
## Contact
Feel free to contact me if there are any questions or issues (Tal
Ridnik, tal.ridnik@alibaba-inc.com).
