Model card and the original license

README.md

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+---
+base_model: ZhengPeng7/BiRefNet_lite
+base_model_relation: quantized
+license: mit
+pipeline_tag: image-segmentation
+library_name: mlx
+tags:
+- mlx
+- moonbox
+- birefnet
+- background-removal
+- dichotomous-image-segmentation
+---
+
+# BiRefNet-lite MLX
+
+MLX builds of [BiRefNet-lite](https://huggingface.co/ZhengPeng7/BiRefNet_lite), made by Moonbox for Apple Silicon Macs. Each precision is a folder; keep a folder's
+files together. Only the parts listed below are converted: the rest of the pipeline comes from the original
+repository unless the section below says otherwise.
+
+## Files
+
+The last column is how far each build is from the original PyTorch model on the same inputs (see the section below
+for what was measured).
+
+| Precision | File | Size | sha256 | Checked against the original |
+|---|---|---|---|---|
+| fp16 | [`fp16/config.json`](https://huggingface.co/moonbox-ai/BiRefNet_lite-MLX/blob/main/fp16/config.json) | 0.1 kB | `22466f2eeac0` | MAE <= 0.00002, mask IoU >= 0.9998 |
+| fp16 | [`fp16/mlx_model.py`](https://huggingface.co/moonbox-ai/BiRefNet_lite-MLX/blob/main/fp16/mlx_model.py) | 20.1 kB | `bd3cb5681703` | MAE <= 0.00002, mask IoU >= 0.9998 |
+| fp16 | [`fp16/model.safetensors`](https://huggingface.co/moonbox-ai/BiRefNet_lite-MLX/blob/main/fp16/model.safetensors) | 88.6 MB | `dedef2f909b6` | MAE <= 0.00002, mask IoU >= 0.9998 |
+
+## How to run
+
+BiRefNet-lite ported to MLX. Each folder holds `model.safetensors`, `config.json` and `mlx_model.py`, the port (only
+`mlx`, `numpy` and `pillow` needed):
+
+```python
+import numpy as np, mlx_model
+from PIL import Image
+model = mlx_model.load("fp16")
+rgb = np.array(Image.open("in.jpg").convert("RGB"))
+alpha = mlx_model.matte(model, rgb)                   # HxW uint8; the input is resized to 1024x1024 as in handler.py
+Image.fromarray(np.dstack([rgb, alpha])).save("cutout.png")
+```
+
+The port runs the released configuration (Swin-T at full and half resolution, ASPP-deformable decoder) with the
+BatchNorms folded into the convolutions and torchvision's `deform_conv2d` written out in MLX. The quantized folders
+quantize the Swin linears whose input width is a multiple of 64 (group size 64); the convolutions, the first Swin
+stage and the norms stay bf16, so they save less than the bit width suggests.
+
+| Precision | Size | MAE vs PyTorch fp32 (worst image) | Mask IoU (worst) |
+|---|---|---|---|
+| `fp16/` | 88.7 MB | 0.00002 | 0.9998 |
+| `bf16/` | 88.7 MB | 0.0002 | 0.9987 |
+| `8bit/` | 63.1 MB | 0.0002 | 0.9978 |
+| `6bit/` | 56.3 MB | 0.0003 | 0.9986 |
+| `4bit/` | 49.5 MB | 0.0010 | 0.9845 |
+
+About 0.75 s per image (1024x1024) on an Apple M5 at every precision.
+
+## How these were made
+
+From [BiRefNet-lite](https://huggingface.co/ZhengPeng7/BiRefNet_lite) at revision `aa62cd87ea`: the original weights loaded in MLX and stored as bf16 or fp16, or
+quantized with MLX's affine quantization (`8bit`, `6bit`, `4bit`, group size 64; layers MLX can't quantize, such as
+convolutions and norms, stay bf16). Every build was checked against the original before upload; the `sha256` column
+above is what the files must hash to.
+
+## License
+
+MIT. These files are a derivative of the original model and carry its license: it is copied in this
+repository, and it governs your use of these files. The original model card follows unchanged.
+
+---
+
+# Original model card
+
+<h1 align="center">Bilateral Reference for High-Resolution Dichotomous Image Segmentation</h1>
+
+<div align='center'>
+    <a href='https://scholar.google.com/citations?user=TZRzWOsAAAAJ' target='_blank'><strong>Peng Zheng</strong></a><sup> 1,4,5,6</sup>,&thinsp;
+    <a href='https://scholar.google.com/citations?user=0uPb8MMAAAAJ' target='_blank'><strong>Dehong Gao</strong></a><sup> 2</sup>,&thinsp;
+    <a href='https://scholar.google.com/citations?user=kakwJ5QAAAAJ' target='_blank'><strong>Deng-Ping Fan</strong></a><sup> 1*</sup>,&thinsp;
+    <a href='https://scholar.google.com/citations?user=9cMQrVsAAAAJ' target='_blank'><strong>Li Liu</strong></a><sup> 3</sup>,&thinsp;
+    <a href='https://scholar.google.com/citations?user=qQP6WXIAAAAJ' target='_blank'><strong>Jorma Laaksonen</strong></a><sup> 4</sup>,&thinsp;
+    <a href='https://scholar.google.com/citations?user=pw_0Z_UAAAAJ' target='_blank'><strong>Wanli Ouyang</strong></a><sup> 5</sup>,&thinsp;
+    <a href='https://scholar.google.com/citations?user=stFCYOAAAAAJ' target='_blank'><strong>Nicu Sebe</strong></a><sup> 6</sup>
+</div>
+
+<div align='center'>
+    <sup>1 </sup>Nankai University&ensp;  <sup>2 </sup>Northwestern Polytechnical University&ensp;  <sup>3 </sup>National University of Defense Technology&ensp; <sup>4 </sup>Aalto University&ensp;  <sup>5 </sup>Shanghai AI Laboratory&ensp;  <sup>6 </sup>University of Trento&ensp; 
+</div>
+
+<div align="center" style="display: flex; justify-content: center; flex-wrap: wrap;">
+  <a href='https://arxiv.org/pdf/2401.03407'><img src='https://img.shields.io/badge/arXiv-BiRefNet-red'></a>&ensp; 
+  <a href='https://drive.google.com/file/d/1aBnJ_R9lbnC2dm8dqD0-pzP2Cu-U1Xpt/view?usp=drive_link'><img src='https://img.shields.io/badge/中文版-BiRefNet-red'></a>&ensp; 
+  <a href='https://www.birefnet.top'><img src='https://img.shields.io/badge/Page-BiRefNet-red'></a>&ensp; 
+  <a href='https://drive.google.com/drive/folders/1s2Xe0cjq-2ctnJBR24563yMSCOu4CcxM'><img src='https://img.shields.io/badge/Drive-Stuff-green'></a>&ensp; 
+  <a href='LICENSE'><img src='https://img.shields.io/badge/License-MIT-yellow'></a>&ensp; 
+  <a href='https://huggingface.co/spaces/ZhengPeng7/BiRefNet_demo'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20HF%20Spaces-BiRefNet-blue'></a>&ensp; 
+  <a href='https://huggingface.co/ZhengPeng7/BiRefNet'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20HF%20Models-BiRefNet-blue'></a>&ensp; 
+  <a href='https://colab.research.google.com/drive/14Dqg7oeBkFEtchaHLNpig2BcdkZEogba?usp=drive_link'><img src='https://img.shields.io/badge/Single_Image_Inference-F9AB00?style=for-the-badge&logo=googlecolab&color=525252'></a>&ensp; 
+  <a href='https://colab.research.google.com/drive/1MaEiBfJ4xIaZZn0DqKrhydHB8X97hNXl#scrollTo=DJ4meUYjia6S'><img src='https://img.shields.io/badge/Inference_&_Evaluation-F9AB00?style=for-the-badge&logo=googlecolab&color=525252'></a>&ensp; 
+</div>
+
+
+|            *DIS-Sample_1*        |             *DIS-Sample_2*        |
+| :------------------------------: | :-------------------------------: |
+| <img src="https://drive.google.com/thumbnail?id=1ItXaA26iYnE8XQ_GgNLy71MOWePoS2-g&sz=w400" /> |  <img src="https://drive.google.com/thumbnail?id=1Z-esCujQF_uEa_YJjkibc3NUrW4aR_d4&sz=w400" /> |
+
+This repo is the official implementation of "[**Bilateral Reference for High-Resolution Dichotomous Image Segmentation**](https://arxiv.org/pdf/2401.03407.pdf)" (___CAAI AIR 2024___). 
+
+Visit our GitHub repo: [https://github.com/ZhengPeng7/BiRefNet](https://github.com/ZhengPeng7/BiRefNet) for more details -- **codes**, **docs**, and **model zoo**!
+
+## How to use (this tiny version)
+
+### 0. Install Packages:
+```
+pip install -qr https://raw.githubusercontent.com/ZhengPeng7/BiRefNet/main/requirements.txt
+```
+
+### 1. Load BiRefNet:
+
+#### Use codes + weights from HuggingFace
+> Only use the weights on HuggingFace -- Pro: No need to download BiRefNet codes manually; Con: Codes on HuggingFace might not be latest version (I'll try to keep them always latest).
+
+```python
+# Load BiRefNet with weights
+from transformers import AutoModelForImageSegmentation
+birefnet = AutoModelForImageSegmentation.from_pretrained('zhengpeng7/BiRefNet_lite', trust_remote_code=True)
+```
+
+#### Use codes from GitHub + weights from HuggingFace
+> Only use the weights on HuggingFace -- Pro: codes are always the latest; Con: Need to clone the BiRefNet repo from my GitHub.
+
+```shell
+# Download codes
+git clone https://github.com/ZhengPeng7/BiRefNet.git
+cd BiRefNet
+```
+
+```python
+# Use codes locally
+from models.birefnet import BiRefNet
+
+# Load weights from Hugging Face Models
+### >>> Remember to set the `bb` in `config.py` as `swin_v1_t` to use this tiny version. <<< ###
+birefnet = BiRefNet.from_pretrained('zhengpeng7/BiRefNet_lite')
+```
+
+#### Use codes from GitHub + weights from local space
+> Only use the weights and codes both locally.
+
+```python
+# Use codes and weights locally
+### >>> Remember to set the `bb` in `config.py` as `swin_v1_t` to use this tiny version. <<< ###
+import torch
+from utils import check_state_dict
+
+birefnet = BiRefNet(bb_pretrained=False)
+state_dict = torch.load(PATH_TO_WEIGHT, map_location='cpu')
+state_dict = check_state_dict(state_dict)
+birefnet.load_state_dict(state_dict)
+```
+
+#### Use the loaded BiRefNet for inference
+```python
+# Imports
+from PIL import Image
+import matplotlib.pyplot as plt
+import torch
+from torchvision import transforms
+from models.birefnet import BiRefNet
+
+birefnet = ... # -- BiRefNet should be loaded with codes above, either way.
+torch.set_float32_matmul_precision(['high', 'highest'][0])
+birefnet.to('cuda')
+birefnet.eval()
+birefnet.half()
+
+def extract_object(birefnet, imagepath):
+    # Data settings
+    image_size = (1024, 1024)
+    transform_image = transforms.Compose([
+        transforms.Resize(image_size),
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+    image = Image.open(imagepath)
+    input_images = transform_image(image).unsqueeze(0).to('cuda').half()
+
+    # Prediction
+    with torch.no_grad():
+        preds = birefnet(input_images)[-1].sigmoid().cpu()
+    pred = preds[0].squeeze()
+    pred_pil = transforms.ToPILImage()(pred)
+    mask = pred_pil.resize(image.size)
+    image.putalpha(mask)
+    return image, mask
+
+# Visualization
+plt.axis("off")
+plt.imshow(extract_object(birefnet, imagepath='PATH-TO-YOUR_IMAGE.jpg')[0])
+plt.show()
+
+```
+
+
+> This BiRefNet for standard dichotomous image segmentation (DIS) is trained on **DIS-TR** and validated on **DIS-TEs and DIS-VD**.
+
+## This repo holds the official model weights of "[<ins>Bilateral Reference for High-Resolution Dichotomous Image Segmentation</ins>](https://arxiv.org/pdf/2401.03407)" (_CAAI AIR 2024_).
+
+This repo contains the weights of BiRefNet proposed in our paper, which has achieved the SOTA performance on three tasks (DIS, HRSOD, and COD).
+
+Go to my GitHub page for BiRefNet codes and the latest updates: https://github.com/ZhengPeng7/BiRefNet :)
+
+
+#### Try our online demos for inference:
+
++ Online **Single Image Inference** on Colab: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/14Dqg7oeBkFEtchaHLNpig2BcdkZEogba?usp=drive_link)
++ **Online Inference with GUI on Hugging Face** with adjustable resolutions: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/ZhengPeng7/BiRefNet_demo)  
++ **Inference and evaluation** of your given weights: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1MaEiBfJ4xIaZZn0DqKrhydHB8X97hNXl#scrollTo=DJ4meUYjia6S)
+<img src="https://drive.google.com/thumbnail?id=12XmDhKtO1o2fEvBu4OE4ULVB2BK0ecWi&sz=w1080" />
+
+## Acknowledgement:
+
++ Many thanks to @fal for their generous support on GPU resources for training better BiRefNet models.
++ Many thanks to @not-lain for his help on the better deployment of our BiRefNet model on HuggingFace.
+
+
+## Citation
+
+```
+@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}
+}
+```