SegVol-for-SegFM

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SegVol-for-SegFM

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# SegVol-for-SegFM
This repo is the SegFM version of SegVol for [SegFM](https://www.codabench.org/competitions/5263/).
SegFM is CVPR 2025: Foundation Models for Interactive 3D Biomedical Image Segmentation.

Origin repo of SegVol: https://github.com/BAAI-DCAI/SegVol

CVPR Descriptive Video: [CVPR25SegFM Webinar: 05 SegVol | Yuxin Du](https://youtu.be/Kt-2XfZFeA8?si=WJ95IPsEjgVc59vv)
### Inference Map
![segvol pre](https://github.com/user-attachments/assets/76be187f-6379-45ee-9249-b3d416cbfdac)
### Training Map
![segvvol train](https://github.com/user-attachments/assets/c0dfaa55-2a5b-444f-a091-f4083b65cbfd)

## Get start with source code
### Requirements
The [pytorch v1.13.1](https://pytorch.org/get-started/previous-versions/) (or a higher version) is needed first. Following install key requirements using commands:

```
pip install 'monai[all]==0.9.0'
pip install einops==0.6.1
pip install transformers==4.18.0
```
OR

```
conda env create -f environment.yml
```

### Validation
1. Download our preliminary [**checkpoint** (2000 epochs ckpt)](https://drive.google.com/file/d/1dgM5slT5kDV3D_6k_vGpGqU5yB1nTwCL/view?usp=drive_link) first and set the `ckpt_path` var in validation.py. (Trained on 10% dataset and support box prompt only)

3. Download [SegFM](https://www.codabench.org/competitions/5263/) validation set and build **val_samples.json** file to index validation npz files like this:
```
[
"/path/to/xxx.npz",
"/path/to/yyy.npz"
]
```

3. Run:
```
python validation.py
```

### Training
#### GPU memory requirement for finetuning: 24GB for 4 batch size
1. **Train based on the FM3D10% 2000 epochs SegVol checkpoint:** Download our preliminary [**checkpoint** (2000 epochs ckpt)](https://drive.google.com/file/d/1dgM5slT5kDV3D_6k_vGpGqU5yB1nTwCL/view?usp=drive_link) first and set the `resume_checkpoint` var in train.py. (Trained on 10% dataset and support box prompt only)

**OR**

**Train based on original SegVol checkpoint:** Download the original [**checkpoint**](https://drive.google.com/file/d/1FPj_tiITss5vJF91SrfPEURH6CUEmo4u/view?usp=sharing) first and set the `resume_checkpoint` var in train.py.
3. Download [SegFM](https://www.codabench.org/competitions/5263/) ALL or 10% trainset, set `train_root_path` var in train.py.
4. Download [SegFM](https://www.codabench.org/competitions/5263/) validation set and build **val_samples.json** file to index validation npz files like this:

(Sampling to reduce the val duration)
```
[
"/path/to/xxx.npz",
"/path/to/yyy.npz"
]
```
4. Run
```
torchrun --nproc_per_node=N train.py
```

## Evaluation with Docker Image
Download and load the [docker image (FM3D10% 2000 epochs SegVol) passwd:fm3d](https://pan.baidu.com/s/1t0_26nGrgt10QQudGAlxew?pwd=fm3d), place all test cts.npz in `inputs`, and run the command:
```
docker container run --gpus "device=0" -m 8G --name segvol --rm -v $PWD/inputs/:/workspace/inputs/ -v $PWD/outputs/:/workspace/outputs/ segvol:lastest
```
The prediction results will be saved in `outputs`

SegVol-for-SegFM

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