To evaluate the results, please upload the zip file to the competition server.
First, inference using the trained model.
python3 inference_ytvos.py --with_box_refine --binary --freeze_text_encoder --output_dir=[/path/to/output_dir] --resume=[/path/to/model_weight] --backbone [backbone]
python3 inference_ytvos.py --with_box_refine --binary --freeze_text_encoder --output_dir=ytvos_dirs/swin_tiny --resume=ytvos_swin_tiny.pth --backbone swin_t_p4w7
If you want to visualize the predited masks, you may add --visualize to the above command.
Then, enter the output_dir, rename the folder valid as Annotations. Use the following command to zip the folder:
zip -q -r submission.zip Annotations
To evaluate the results, please upload the zip file to the competition server.
- Finetune
The following command includes the training and inference stages.
./scripts/dist_train_test_ytvos.sh [/path/to/output_dir] [/path/to/pretrained_weight] --backbone [backbone]
For example, training the Video-Swin-Tiny model, run the following command:
./scripts/dist_train_test_ytvos.sh ytvos_dirs/video_swin_tiny pretrained_weights/video_swin_tiny_pretrained.pth --backbone video_swin_t_p4w7
- Train from scratch
The following command includes the training and inference stages.
./scripts/dist_train_test_ytvos_scratch.sh [/path/to/output_dir] --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_test_ytvos.sh ytvos_dirs/video_swin_tiny_scratch --backbone video_swin_t_p4w7 --backbone_pretrained video_swin_pretrained/swin_tiny_patch244_window877_kinetics400_1k.pth