| --- |
| license: mit |
| library_name: litert |
| pipeline_tag: image-segmentation |
| base_model: ZhengPeng7/BiRefNet_lite |
| tags: |
| - litert |
| - tflite |
| - fp32 |
| - background-removal |
| - foreground-segmentation |
| - birefnet |
| --- |
| |
| # BiRefNet-Lite for LiteRT |
|
|
| FP32 LiteRT/TFLite export of BiRefNet Lite for foreground segmentation and background removal. The model returns a soft foreground mask with the final sigmoid included. |
|
|
| Source checkpoint: [ZhengPeng7/BiRefNet_lite](https://huggingface.co/ZhengPeng7/BiRefNet_lite), revision [`aa62cd87eafb9cc43056d08ef3615a14628b831d`](https://huggingface.co/ZhengPeng7/BiRefNet_lite/tree/aa62cd87eafb9cc43056d08ef3615a14628b831d). |
|
|
| ## Model file |
|
|
| [BiRefNet-Lite-fp32.tflite](BiRefNet-Lite-fp32.tflite) · FP32 · 184.00 MB. The file hash is in [SHA256SUMS](SHA256SUMS). |
|
|
| The TFLite signature is `main`. All input and output tensors are `float32`; shapes are fixed. Image/video decoding, preprocessing, and application postprocessing run on the host. |
|
|
| | Direction | Name | Shape | Meaning | |
| | --- | --- | --- | --- | |
| | Input | `pixel_values` | `[1, 3, 1024, 1024]` | Normalized RGB, NCHW | |
| | Output | `foreground_probability` | `[1, 1, 1024, 1024]` | Foreground probability in [0, 1], NCHW | |
|
|
| ## Preprocessing and outputs |
|
|
| Convert the image to RGB, resize to **1024 × 1024** with PIL bilinear interpolation, divide by 255, and normalize with ImageNet mean `[0.485, 0.456, 0.406]` and standard deviation `[0.229, 0.224, 0.225]`. Transpose to NCHW. Resize the output probability map to the original image size for an alpha mask. |
|
|
| ## Run on CPU |
|
|
| ```bash |
| pip install ai-edge-litert huggingface_hub numpy pillow |
| ``` |
|
|
| ```python |
| import numpy as np |
| from huggingface_hub import hf_hub_download |
| from ai_edge_litert.interpreter import Interpreter |
| from PIL import Image |
| |
| image = Image.open("image.jpg").convert("RGB") |
| rgb = np.asarray(image.resize((1024, 1024), Image.Resampling.BILINEAR), |
| dtype=np.float32) / 255.0 |
| mean = np.array([0.485, 0.456, 0.406], dtype=np.float32) |
| std = np.array([0.229, 0.224, 0.225], dtype=np.float32) |
| pixel_values = np.ascontiguousarray(((rgb - mean) / std).transpose(2, 0, 1)[None]) |
| |
| model_path = hf_hub_download("litert-community/BiRefNet-Lite", "BiRefNet-Lite-fp32.tflite") |
| interpreter = Interpreter(model_path=model_path, num_threads=4) |
| interpreter.allocate_tensors() |
| run = interpreter.get_signature_runner("main") |
| probability = run(pixel_values=pixel_values)["foreground_probability"][0, 0] |
| # Restore the soft foreground mask to the original image dimensions. |
| mask = Image.fromarray(probability).resize(image.size, Image.Resampling.BILINEAR) |
| alpha = Image.fromarray(np.rint(np.clip(np.asarray(mask), 0, 1) * 255).astype(np.uint8)) |
| rgba = image.convert("RGBA") |
| rgba.putalpha(alpha) |
| rgba.save("foreground.png") |
| ``` |
|
|
| ## Source and license |
|
|
| BiRefNet is by Peng Zheng and collaborators. See the [project](https://github.com/ZhengPeng7/BiRefNet) and [paper](https://arxiv.org/abs/2401.03407). |
|
|
| This FP32 conversion uses the original pretrained weights with a fixed-shape TFLite interface. License: [MIT](LICENSE). |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{zheng2024birefnet, |
| title={Bilateral Reference for High-Resolution Dichotomous Image Segmentation}, |
| author={Zheng, Peng and Gao, Dehong and Fan, Deng-Ping and Liu, Li and Laaksonen, Jorma and Ouyang, Wanli and Sebe, Nicu}, |
| journal={CAAI Artificial Intelligence Research}, |
| volume={3}, |
| pages={9150038}, |
| year={2024} |
| } |
| ``` |
|
|
