DINOv2_mmseg: Segmentation downstream of DINOv2 using mmsegmentation

Training and testing of DINOv2 for segmentation downstream

Image source: Semantic Foggy Scene Understanding with Synthetic Data

⚙️ Prerequisites

• Clone DINOv2 repo and edit train.py, test.py, and models/backbones/vit_dinov2.py to change DINOv2's path. Specifically, replace the line sys.path.append("../prototyping_dinov2") with your path to DINOv2. This is required in particular if you get the error KeyError: "EncoderDecoder: 'DinoVisionTransformer is not in the models registry'".

  Alternatively, you can python -m pip install -e <path-to-dinov2> and remove the sys.path.append("../prototyping_dinov2") lines from all scripts.

• Install dependencies from DINOv2: https://github.com/facebookresearch/dinov2/blob/main/conda-extras.yaml

  conda env create -f conda.yaml
  conda activate dinov2

• Install mmcv>=2.0 and mmsegmentation >= 1.0. Check https://mmsegmentation.readthedocs.io/en/latest/get_started.html

Training

1. An example config is provided for the Cityscapes dataset in configs/dinov2_vitb14_cityscapes_ms_config.py. Edit the config accoording to your preferences. Refer https://mmsegmentation.readthedocs.io/en/latest/user_guides/1_config.html for more details on the config.

• To load the pretrained backbone and freeze it, change the config's model.pretrained to the path to pretrained backbone weights and model.backbone.freeze_vit = True to freeze it.

• To load a complete (backbone + head) pretrained model, change the config's load_from to the path to the pretrained model file. Alternatively, resume=True with automatically find the last_checkpoint from your log_dir and resume training.

2. To run training, do CUDA_VISIBLE_DEVICES=1,2,3 python train.py <path-to-config> . Refer https://mmsegmentation.readthedocs.io/en/latest/user_guides/4_train_test.html

Testing

3. To run test, do CUDA_VISIBLE_DEVICES=1,2,3 python test.py <path-to-config> <path-to-checkpoint>. Refer https://mmsegmentation.readthedocs.io/en/latest/user_guides/4_train_test.html

Inference

Checkout segmentation.ipynb for an inference example.

👩‍⚖️ Licence

train.py modified from https://github.com/open-mmlab/mmsegmentation/blob/main/tools/train.py test.py modified from https://github.com/open-mmlab/mmsegmentation/blob/main/tools/test.py

train.py and test.py are modified from mmsegmentation and therefore respect mmsegmentation's licence.

The rest is licenced under Apache License 2.0, Copyright © Zeeshan Khan Suri, Denso ADAS Engineering Services GmbH, 2024.