| # Training | |
| **Re-implemented training codes in public environments by @JUGGHM** | |
| This is an re-implemented and verified version of the original training codes in private environments. Codes for overall framework, dataloaders, and losses are kept. | |
| However, we cannot provide the annotations ```json``` currently due to IP issues. | |
| You can either integrate our framework into your own codes (Recommanded), or prepare the datasets as following (Needs many efforts). | |
| ### Config the pretrained checkpoints for ConvNeXt and DINOv2 | |
| Download the checkpoints and config the paths in ```data_server_info/pretrained_weight.py``` | |
| ### Prepare the json files | |
| Prepare json files for different datasets in ```data_server_info/public_datasets.py```. Some tiny examples are also provided in ```data_server_info/annos*.json```. | |
| ### Train | |
| ```bash mono/scripts/training_scripts/train.sh``` | |