Add model

BiRefNet-Lite-fp32.tflite

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ddfafe4f881ea08dca4bef19391136989a21a53aea12fc639d275fd4c643677d
+size 184001424

LICENSE

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+MIT License
+
+Copyright (c) 2024 ZhengPeng
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+---
+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}
+}
+```

SHA256SUMS

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+ddfafe4f881ea08dca4bef19391136989a21a53aea12fc639d275fd4c643677d  BiRefNet-Lite-fp32.tflite