x-segment-anything 0.0.8
Segment Anything Model (SAM) modules combined in the same API (to make life easier).
License: MIT License
Requires Python: >=3.8
Keywords: xSAM

# xSAM

Segment Anything Model (SAM) variants including:
- CoralSCOP
- RepViT-SAM 
- EdgeSAM
- MobileSAM
- SAM (original) 

combined in the same API (to make life easier).

For more information on different SAM variants, please see the following:
- [_On Efficient Variants of Segment Anything Model: A Survey_](https://arxiv.org/html/2410.04960v1)

## Installation

The code requires `python>=3.8`, as well as `pytorch>=1.7` and `torchvision>=0.8`. 
Please follow the instructions [here](https://pytorch.org/get-started/locally/) to install both PyTorch and TorchVision 
dependencies. Installing both PyTorch and TorchVision with CUDA support is strongly recommended.

Install xSAM:

```bash
pip install x-segment-anything
```

## Getting Started
The SAM models can be loaded in the following ways:

```python
from x_segment_anything import sam_model_registry, SamPredictor

model_type = "vit_b_coralscop"
model_type = "repvit"
model_type = "edge_sam"
model_type = "vit_t"
model_type = "vit_b"
model_type = "vit_l"
model_type = "vit_h"

sam_checkpoint = "path_to_checkpoints/model_x_weights.pt"

device = "cuda" if torch.cuda.is_available() else "cpu"

x_sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)
x_sam.to(device=device)
x_sam.eval()

predictor = SamPredictor(x_sam)
predictor.set_image(<your_image>)
masks, _, _ = predictor.predict(<input_prompts>)
```

or generate masks for an entire image:

```python
from x_segment_anything import SamAutomaticMaskGenerator

mask_generator = SamAutomaticMaskGenerator(x_sam)
masks = mask_generator.generate(<your_image>)
```

## Model Checkpoints
For convenience, The following model checkpoints are available in the `sam_model_urls` dictionary and can be downloaded 
in python:

```python
import requests
from x_segment_anything.build_sam import sam_model_urls

def download_asset(asset_url, asset_path):
    response = requests.get(asset_url)
    with open(asset_path, 'wb') as f:
        f.write(response.content)

model_path = "vit_b_coral_scop.pt"
model_path = "repvit.pt"
model_path = "edge_sam.pt"
model_path = "edge_sam_3x.pt"
model_path = "vit_t.pt"
model_path = "vit_b.pt"
model_path = "vit_l.pt"
model_path = "vit_h.pt"

model = model_path.split(".")[0]
model_url = sam_model_urls[model]

download_asset(model_url, model_path)

```

### Model Checkpoint URLs:
- [coralscop](https://github.com/Jordan-Pierce/CoralSCOP/releases/download/v0.0.1/vit_b_coralscop.pth)
- [repvit](https://huggingface.co/spaces/jameslahm/repvit-sam/resolve/main/repvit_sam.pt)
- [edge_sam](https://huggingface.co/spaces/chongzhou/EdgeSAM/resolve/main/weights/edge_sam.pth)
- [edge_sam_3x](https://huggingface.co/spaces/chongzhou/EdgeSAM/resolve/main/weights/edge_sam_3x.pth)
- [vit_t](https://huggingface.co/spaces/dhkim2810/MobileSAM/resolve/main/mobile_sam.pt)
- [vit_b](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth)
- [vit_l](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth)
- [vit_h](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth)

### Acknowledgements:
- [CoralSCOP](https://github.com/zhengziqiang/CoralSCOP)
- [RepViT-SAM](https://github.com/THU-MIG/RepViT/tree/main)
- [EdgeSAM](https://github.com/chongzhou96/EdgeSAM)
- [MobileSAM](https://github.com/ChaoningZhang/MobileSAM)
- [SAM](https://github.com/facebookresearch/segment-anything)

--- 
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