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

Example: Finetune on KITTI

We just re-implemented the json generating script in public environments. Users can finetune the pretrained models on KITTI now with eigen_split json.

Step1: Configure the path for KITTI Raw dataset, KITTI Depth dataset, and the provided json file in data_server_info/public_datasets.py correctly.

Step2: Start training by bash mono/scripts/training_scripts/train_kitti.sh