This repository contains a simple starter code for finetuning the FAIR Segment Anything (SAM) models leveraging the convenience of PyTorch Lightning.

1. Install dependencies

   First run

   git clone --recurse-submodules [email protected]:bhpfelix/segment-anything-finetuner.git

   Then

   cd segment-anything-finetuner

   Follow the setup instruction of Segment Anything to install the proper dependencies. Then run

   pip install -r requirements.txt

2. Data preparation

   The starter code supports Coco format input with the following layout

   ├── dataset_name/
   │   ├── train/
   │   │   ├── _annotations.coco.json # COCO format annotation
   │   │   ├── 000001.png             # Images
   │   │   ├── 000002.png
   │   │   ├── ...
   │   ├── val/
   │   │   ├── _annotations.coco.json # COCO format annotation
   │   │   ├── xxxxxx.png             # Images
   │   │   ├── ...

3. Download model checkpoints

   Download the necessary SAM model checkpoints and arrange the repo as follows:

   ├── dataset_name/              # structure as detailed above
   │   ├── ...
   ├── segment-anything/          # The FAIR SAM repo
   │   ├── ...
   ├── SAM/                       # the SAM pretrained checkpoints
   │   ├── sam_vit_h_4b8939.pth
   │   ├── ...
   ├── finetune.py
   ├── ...

This file contains a simple finetuning script for the Segment Anything model on Coco format datasets.

Example usage:

python finetune.py \
    --data_root ./dataset_name \
    --model_type vit_h \
    --checkpoint_path ./SAM/sam_vit_h_4b8939.pth \
    --freeze_image_encoder \
    --batch_size 2 \
    --image_size 1024 \
    --steps 1500 \
    --learning_rate 1.e-5 \
    --weight_decay 0.01

We can optionally use the --freeze_image_encoder flag to detach the image encoder parameters from optimization and save GPU memory.

• As of now the image resizing implementation is different from the ResizeLongestSide transform in SAM.
• Drop path and layer-wise learning rate decay are not currently applied.
• The finetuning script currently only supports bounding box input prompts.

• Segment Anything
• Segmentation Models
• Detectron2

@article{kirillov2023segany,
  title={Segment Anything},
  author={Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C. and Lo, Wan-Yen and Doll{\'a}r, Piotr and Girshick, Ross},
  journal={arXiv:2304.02643},
  year={2023}
}