https://github.com/Giansar-Wu/SAMSnake
https://github.com/Giansar-Wu/SAMSnake
Last synced: 1 day ago
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- Host: GitHub
- URL: https://github.com/Giansar-Wu/SAMSnake
- Owner: Giansar-Wu
- License: agpl-3.0
- Created: 2025-02-27T06:49:16.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-03-17T13:44:55.000Z (about 2 months ago)
- Last Synced: 2025-03-17T14:39:23.066Z (about 2 months ago)
- Language: Python
- Size: 6.2 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- Awesome-Segment-Anything - [code
README
# SAMSnake: A Generic Contour-based Instance Segmentation Network Assisted by Efficient Segment Anything Model
## Installation
Please see [INSTALL.md](INSTALL.md).
## Architecture
## Testing
### Prepare weights
1. Download the model weights [here](https://drive.google.com/file/d/15D3Obdj4hs-q9GGom_JpIAAg4LqWS180/view?usp=sharing).
2. Unzip weights to SAMSnake.
```
unzip SAMSnake_weights.zip -d /path/tp/SAMSnake
```
### Testing on SBD
```
# testing segmentation accuracy on SBD
python test.py sbd --checkpoint /path/to/model_sbd.pth
# testing the speed
python test.py sbd --checkpoint /path/to/model_sbd.pth --type speed
```
### Testing on Cityscapes
```
# with cityscapes official evaluator
python test.py cityscapes --checkpoint /path/to/model_cityscapes.pth
# testing segmentation accuracy on Cityscapes with coco evaluator
python test.py cityscapesCoco --checkpoint /path/to/model_cityscapes.pth
# testing the speed
python test.py cityscapesCoco \
--checkpoint /path/to/model_cityscapes.pth --type speed
# testing on test set, run and submit the result file
python test.py cityscapes --checkpoint /path/to/model_cityscapes.pth \
--dataset cityscapes_test
```
### Testing on COCO
```
# testing segmentation accuracy on coco val set
python test.py coco --checkpoint /path/to/model_coco.pth
# testing the speed
python test.py coco --checkpoint /path/to/model_coco.pth --type speed
# testing on coco test-dev set, run and submit data/result/results.json
python test.py coco --checkpoint /path/to/model_coco.pth --dataset coco_test
```
### Testing on KINS
```
# testing segmentation accuracy on KINS
python test.py kitti --checkpoint /path/to/model_kitti.pth
# testing the speed
python test.py kitti --checkpoint /path/to/model_kitti.pth --type speed
```
### Testing on COCOA
```
# testing segmentation accuracy on cocoa val set
python test.py cocoa --checkpoint /path/to/model_cocoa.pth
# testing the speed
python test.py cocoa --checkpoint /path/to/model_cocoa.pth --type speed
```
## Visualization
```
# inference and visualize the images with coco pretrained model
python visualize.py coco /path/to/images \
--checkpoint /path/to/model_coco.pth --with_nms True
# you can using other pretrained model, such as cityscapes
python visualize.py cityscapesCoco /path/to/images \
--checkpoint /path/to/model_cityscapes.pth
# if you want to save the visualisation, please specify --output_dir
python visualize.py coco /path/to/images \
--checkpoint /path/to/model_coco.pth --with_nms True \
--output_dir /path/to/output_dir
# visualize the results at different stage
python visualize.py coco /path/to/images \
--checkpoint /path/to/model_coco.pth --with_nms True --stage coarse
# you can reset the score threshold, default is 0.3
python visualize.py coco /path/to/images \
--checkpoint /path/to/model_coco.pth --with_nms True --ct_score 0.1
# if you want to filter some of the jaggedness caused by dml
# please using post_process
python visualize.py coco /path/to/images \
--checkpoint /path/to/model_coco.pth --with_nms True \
--with_post_process True
```
## Training
### Training with multi GPUS
```
CUDA_VISIBLE_DEVICES=${gpu_ids} torchrun
--standalone \
--nnodes=${nodes_num} \
--nproc-per-node=${gpu_nums_per_node} \
train_net_ddp.py \
--config_file ${dataset} \
--bs ${batchsize} \
# the example of training sbd dataset using 2 gpus
CUDA_VISIBLE_DEVICES=0,1 torchrun \
--standalone \
--nnodes=1 \
--nproc-per-node=2 \
train_net_ddp.py \
--config_file sbd \
--bs 24
```
### Training with single GPU
```
# sbd
python train_net.py sbd --bs $batch_size
# cityscapes
python train_net.py cityscapesCoco --bs $batch_size
# coco
python train_net.py coco --bs $batch_size
# kitti
python train_net.py kitti --bs $batch_size
# cocoa
python train_net.py cocoa --bs $batch_size
```
### Training on the other dataset
If the annotations is in coco style:
1. Add dataset information to `dataset/info.py`.
2. Modify the `configs/coco.py`, reset the `train.dataset` , `model.heads['ct_hm']` and `test.dataset`. Maybe you also need to change the `train.epochs`, `train.optimizer['milestones']` and so on.
3. Train the network.
```
python train_net.py coco --bs $batch_size
```
If the annotations is not in coco style:
1. Prepare `dataset/train/your_dataset.py` and `dataset/test/your_dataset.py` by referring to `dataset/train/base.py` and `dataset/test/base.py`.
2. Prepare `evaluator/your_dataset/snake.py` by referring to `evaluator/coco/snake.py`.
3. Prepare `configs/your_dataset.py` and by referring to `configs/base.py`.
4. Train the network.
```
python train_net.py your_dataset --bs $batch_size
```
## Acknowledgement
Code is largely based on [E2EC](https://github.com/zhang-tao-whu/e2ec). Thanks for their wonderful works.