x-segment-anything 0.0.2

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xSAM

In-house version of EdgeSAM, MobileSAM, and SAM modules combined in the same API (to make life easier).

Installation

The code requires python>=3.8, as well as pytorch>=1.7 and torchvision>=0.8. Please follow the instructions here to install both PyTorch and TorchVision dependencies. Installing both PyTorch and TorchVision with CUDA support is strongly recommended.

Install xSAM:

pip install git+https://github.com/Jordan-Pierce/xSAM.git

Getting Started

The SAM models can be loaded in the following ways:

from x_segment_anything import sam_model_registry, SamPredictor

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

sam_checkpoint = "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:

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:

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 = "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:

• edge_sam
• edge_sam_3x
• vit_t
• vit_b
• vit_l
• vit_h

Acknowledgements:

• EdgeSAM
• MobileSAM
• SAM

Disclaimer

This repository is a scientific product and is not official communication of the National Oceanic and Atmospheric Administration, or the United States Department of Commerce. All NOAA GitHub project code is provided on an 'as is' basis and the user assumes responsibility for its use. Any claims against the Department of Commerce or Department of Commerce bureaus stemming from the use of this GitHub project will be governed by all applicable Federal law. Any reference to specific commercial products, processes, or services by service mark, trademark, manufacturer, or otherwise, does not constitute or imply their endorsement, recommendation or favoring by the Department of Commerce. The Department of Commerce seal and logo, or the seal and logo of a DOC bureau, shall not be used in any manner to imply endorsement of any commercial product or activity by DOC or the United States Government.

License

Software code created by U.S. Government employees is not subject to copyright in the United States (17 U.S.C. §105). The United States/Department of Commerce reserve all rights to seek and obtain copyright protection in countries other than the United States for Software authored in its entirety by the Department of Commerce. To this end, the Department of Commerce hereby grants to Recipient a royalty-free, nonexclusive license to use, copy, and create derivative works of the Software outside of the United States.