opencv/image_segmentation_efficientsam

image_segmentation_efficientsam

EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything

Notes:

• The current implementation of the EfficientSAM demo uses the EfficientSAM-Ti model, which is specifically tailored for scenarios requiring higher speed and lightweight.
• image_segmentation_efficientsam_ti_2024may.onnx(supports only single point infering)
  • MD5 value: 117d6a6cac60039a20b399cc133c2a60
  • SHA-256 value: e3957d2cd1422855f350aa7b044f47f5b3eafada64b5904ed330b696229e2943
• image_segmentation_efficientsam_ti_2025april.onnx
  • MD5 value: f23cecbb344547c960c933ff454536a3
  • SHA-256 value: 4eb496e0a7259d435b49b66faf1754aa45a5c382a34558ddda9a8c6fe5915d77
• image_segmentation_efficientsam_ti_2025april_int8.onnx
  • MD5 value: a1164f44b0495b82e9807c7256e95a50
  • SHA-256 value: 5ecc8d59a2802c32246e68553e1cf8ce74cf74ba707b84f206eb9181ff774b4e

Demo

Python

Run the following command to try the demo:

python demo.py --input /path/to/image

Click to select foreground points, drag to use box to select and long press to select background points on the object you wish to segment in the displayed image. After clicking the Enter, the segmentation result will be shown in a new window. Clicking the Backspace to clear all the prompts.

Result

Here are some of the sample results that were observed using the model:

Video inference result:

Model metrics:

License

All files in this directory are licensed under Apache 2.0 License.

Contributor Details

Reference

• https://arxiv.org/abs/2312.00863
• https://github.com/yformer/EfficientSAM
• https://github.com/facebookresearch/segment-anything