基于深度学习的积灰检测识别-图像分类源码+数据集（高分项目）.zip

# SupContrast: Supervised Contrastive Learning <p align="center"> <img src="figures/teaser.png" width="700"> </p> This repo covers an reference implementation for the following papers in PyTorch, using CIFAR as an illustrative example: (1) Supervised Contrastive Learning. [Paper](https://arxiv.org/abs/2004.11362) (2) A Simple Framework for Contrastive Learning of Visual Representations. [Paper](https://arxiv.org/abs/2002.05709) ## Update ImageNet model (small batch size with the trick of the momentum encoder) is released [here](https://www.dropbox.com/s/l4a69ececk4spdt/supcon.pth?dl=0). ## Loss Function The loss function [`SupConLoss`](https://github.com/HobbitLong/SupContrast/blob/master/losses.py#L11) in `losses.py` takes `features` (L2 normalized) and `labels` as input, and return the loss. If `labels` is `None` or not passed to the it, it degenerates to SimCLR. Usage: ```python from losses import SupConLoss # define loss with a temperature `temp` criterion = SupConLoss(temperature=temp) # features: [bsz, n_views, f_dim] # `n_views` is the number of crops from each image # better be L2 normalized in f_dim dimension features = ... # labels: [bsz] labels = ... # SupContrast loss = criterion(features, labels) # or SimCLR loss = criterion(features) ... ``` ## Comparison Results on CIFAR-10: | |Arch | Setting | Loss | Accuracy(%) | |----------|:----:|:---:|:---:|:---:| | SupCrossEntropy | ResNet50 | Supervised | Cross Entropy | 95.0 | | SupContrast | ResNet50 | Supervised | Contrastive | 96.0 | | SimCLR | ResNet50 | Unsupervised | Contrastive | 93.6 | Results on CIFAR-100: | |Arch | Setting | Loss | Accuracy(%) | |----------|:----:|:---:|:---:|:---:| | SupCrossEntropy | ResNet50 | Supervised | Cross Entropy | 75.3 | | SupContrast | ResNet50 | Supervised | Contrastive | 76.5 | | SimCLR | ResNet50 | Unsupervised | Contrastive | 70.7 | Results on ImageNet (Stay tuned): | |Arch | Setting | Loss | Accuracy(%) | |----------|:----:|:---:|:---:|:---:| | SupCrossEntropy | ResNet50 | Supervised | Cross Entropy | - | | SupContrast | ResNet50 | Supervised | Contrastive | 79.1 (MoCo trick) | | SimCLR | ResNet50 | Unsupervised | Contrastive | - | ## Running You might use `CUDA_VISIBLE_DEVICES` to set proper number of GPUs, and/or switch to CIFAR100 by `--dataset cifar100`. **(1) Standard Cross-Entropy** ``` python main_ce.py --batch_size 1024 \ --learning_rate 0.8 \ --cosine --syncBN \ ``` **(2) Supervised Contrastive Learning** Pretraining stage: ``` python main_supcon.py --batch_size 1024 \ --learning_rate 0.5 \ --temp 0.1 \ --cosine ``` <s>You can also specify `--syncBN` but I found it not crucial for SupContrast (`syncBN` 95.9% v.s. `BN` 96.0%). </s> WARN: Currently, `--syncBN` has no effect since the code is using `DataParallel` instead of `DistributedDataParaleel` Linear evaluation stage: ``` python main_linear.py --batch_size 512 \ --learning_rate 5 \ --ckpt /path/to/model.pth ``` **(3) SimCLR** Pretraining stage: ``` python main_supcon.py --batch_size 1024 \ --learning_rate 0.5 \ --temp 0.5 \ --cosine --syncBN \ --method SimCLR ``` The `--method SimCLR` flag simply stops `labels` from being passed to `SupConLoss` criterion. Linear evaluation stage: ``` python main_linear.py --batch_size 512 \ --learning_rate 1 \ --ckpt /path/to/model.pth ``` On custom dataset: ``` python main_supcon.py --batch_size 1024 \ --learning_rate 0.5 \ --temp 0.1 --cosine \ --dataset path \ --data_folder ./path \ --mean "(0.4914, 0.4822, 0.4465)" \ --std "(0.2675, 0.2565, 0.2761)" \ --method SimCLR ``` The `--data_folder` must be of form ./path/label/xxx.png folowing https://pytorch.org/docs/stable/torchvision/datasets.html#torchvision.datasets.ImageFolder convension. and ## t-SNE Visualization **(1) Standard Cross-Entropy** <p align="center"> <img src="figures/SupCE.jpg" width="400"> </p> **(2) Supervised Contrastive Learning** <p align="center"> <img src="figures/SupContrast.jpg" width="800"> </p> **(3) SimCLR** <p align="center"> <img src="figures/SimCLR.jpg" width="800"> </p> ## Reference ``` @Article{khosla2020supervised, title = {Supervised Contrastive Learning}, author = {Prannay Khosla and Piotr Teterwak and Chen Wang and Aaron Sarna and Yonglong Tian and Phillip Isola and Aaron Maschinot and Ce Liu and Dilip Krishnan}, journal = {arXiv preprint arXiv:2004.11362}, year = {2020}, } ```