Paper | Pretrained models

Official PyTorch Implementation

Emanuel Ben-Baruch, Tal Ridnik, Nadav Zamir, Asaf Noy, Itamar Friedman, Matan Protter, Lihi Zelnik-Manor
DAMO Academy, Alibaba Group

Note - Github page is still under develop. More content soon...

Abstract

Pictures of everyday life are inherently multi-label in nature. Hence, multi-label classification is commonly used to analyze their content. In typical multi-label datasets, each picture contains only a few positive labels, and many negative ones. This positive-negative imbalance can result in under-emphasizing gradients from positive labels during training, leading to poor accuracy. In this paper, we introduce a novel asymmetric loss ("ASL"), that operates differently on positive and negative samples. The loss dynamically down-weights the importance of easy negative samples, causing the optimization process to focus more on the positive samples, and also enables to discard mislabeled negative samples. We demonstrate how ASL leads to a more "balanced" network, with increased average probabilities for positive samples, and show how this balanced network is translated to better mAP scores, compared to commonly used losses. Furthermore, we offer a method that can dynamically adjust the level of asymmetry throughout the training. With ASL, we reach new state-of-the-art results on three common multi-label datasets, including achieving $86.6%$ on MS-COCO. We also demonstrate ASL applicability for other tasks such as fine-grain single-label classification and object detection. ASL is effective, easy to implement, and does not increase the training time or complexity

In this PyTorch file, we provide two implementations of our new loss function, ASL, that can serve as a drop-in replacement for standard loss functions (Cross-Entropy and Focal-Loss)

The two implementations are:

• class AsymmetricLoss(nn.Module)
• class AsymmetricLossOptimized(nn.Module)
  

The two losses are bit-accurate. However, AsymmetricLossOptimized contains a more optimized (and complicated) way of implementing ASL, which minimizes memory allocations, gpu uploading, and favors inplace operations.

In this link, we provide pre-trained models on various dataset.

We provide inference code, that demonstrate how to load our model, pre-process an image and do actuall inference. This code can also serve as a template for validating our mAP scores. Example run of MS-COCO model (after downloading the relevant model):

python -m -infer.py  \
--dataset_type=MS-COCO \
--model_name=tresnet_l \
--model_path=./models_local/MS_COCO_TRresNet_L_448_86.6.pth \
--pic_path=./pics/000000000885.jpg \
--input_size=448

which will result in:

Example run of OpenImages model:

python -m -infer.py  \
--dataset_type=OpenImages \
--model_name=tresnet_l \
--model_path=./models_local/Open_ImagesV6_TRresNet_L_448.pth \
--pic_path=./pics/000000000885.jpg \
--input_size=448

 @misc{benbaruch2020asymmetric, 
        title={Asymmetric Loss For Multi-Label Classification}, 
        author={manuel Ben-Baruch and Tal Ridnik and Nadav Zamir and Asaf Noy and Itamar Friedman and Matan Protter and Lihi Zelnik-Manor}, 
        year={2020}, 
        eprint={2009.14119},
        archivePrefix={arXiv}, 
        primaryClass={cs.CV} }

Feel free to contact if there are any questions or issues - Emanuel Ben-Baruch ([email protected]) or Tal Ridnik ([email protected]).