Ref-YouTube-VOS.md
May 27, 2023 ยท View on GitHub
Ref-YouTube-VOS
To evaluate the results, please upload the zip file to the competition server.
| Backbone | J&F | J | F | Model | Submission |
|---|---|---|---|---|---|
| ResNet-50 | 61.9 | 60.4 | 63.4 | model | link |
| ResNet-101 | 63.6 | 61.8 | 65.4 | model | link |
| Swin-L | 68.4 | 66.4 | 70.4 | model | link |
| Video-Swin-T | 64.0 | 62.2 | 65.8 | model | link |
| Video-Swin-S | 65.1 | 63.0 | 67.1 | model | link |
| Video-Swin-B | 67.5 | 65.4 | 69.6 | model | link |
| ConvNext-L | 66.7 | 64.8 | 68.7 | model | link |
| ConvMAE-B | 66.9 | 64.7 | 69.1 | model | link |
Training
./scripts/dist_train.sh --backbone [backbone] --backbone_pretrained [/path/to/backbone_pretrained_weight] [other args]
For example, training the Video-Swin-Tiny model, run the following command:
./scripts/dist_train.sh --backbone video_swin_t_p4w7 --backbone_pretrained video_swin_pretrained/swin_tiny_patch244_window877_kinetics400_1k.pth
Inference & Evaluation
Inference using the trained model.
./scripts/dist_test_ytvos.sh [backbone]
For example, evaluating the Swin-Large model, run the following command:
./scripts/dist_test_ytvos.sh swin_l_p4w7
To evaluate the results, please upload the zip file to the competition server.
Note that, if you use the weights we provide, you should put the weights in the corresponding path. ./results/[backbone]/ckpt/backbone_weight.pth