Model card and the original license

README.md

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+---
+base_model: ZhengPeng7/BiRefNet
+base_model_relation: quantized
+license: mit
+pipeline_tag: image-segmentation
+library_name: onnx
+tags:
+- onnx
+- moonbox
+- birefnet
+- background-removal
+- image-segmentation
+- dichotomous-image-segmentation
+---
+
+# BiRefNet ONNX
+
+ONNX builds of [BiRefNet](https://huggingface.co/ZhengPeng7/BiRefNet), made by Moonbox for on-device use with ONNX Runtime (CUDA, DirectML, CPU). The
+model is a single graph: each precision is one folder holding `model.onnx`.
+
+## Files
+
+The last column is the benchmark: each precision's output against the fp32 graph's on the same eval pictures, and
+its own score on the eval set (details under *How to run*).
+
+| Precision | File | Size | sha256 | Benchmark |
+|---|---|---|---|---|
+| fp16 | [`fp16/model.onnx`](https://huggingface.co/moonbox-dev/BiRefNet-ONNX/blob/main/fp16/model.onnx) | 0.49 GB | `323232ec73a0` | vs fp32: IoU 0.9981, MAE 0.0002; DIS5K MAE 0.0141, S 0.953 |
+
+## How to run
+
+BiRefNet is a general-purpose dichotomous segmentation model (objects, people, fine structure). Made from the official fp32 ONNX `BiRefNet-general-epoch_244.onnx` from [BiRefNet's GitHub release v1](https://github.com/ZhengPeng7/BiRefNet/releases/tag/v1) (the general-use model); its single output is the matte (sigmoid applied).
+
+**ONNX Runtime** (1024 x 1024 input, RGB in 0-1, ImageNet-normalized):
+
+```python
+import numpy as np, onnxruntime as ort
+from PIL import Image
+
+sess = ort.InferenceSession("int8/model.onnx", providers=["CUDAExecutionProvider", "CPUExecutionProvider"])
+img = Image.open("photo.jpg").convert("RGB")
+x = np.asarray(img.resize((1024, 1024), Image.BILINEAR), np.float32) / 255
+x = (x - [0.485, 0.456, 0.406]) / [0.229, 0.224, 0.225]   # ImageNet normalization
+x = x.transpose(2, 0, 1)[None]
+matte = sess.run(None, {sess.get_inputs()[0].name: x})[0][0, 0]
+alpha = Image.fromarray((np.clip(matte, 0, 1) * 255).astype(np.uint8)).resize(img.size, Image.BILINEAR)
+img.putalpha(alpha); img.save("cutout.png")
+```
+
+**Precisions.** fp16: weights and math in fp16, inputs and outputs fp32. int8: weights int8 per output channel behind DequantizeLinear, activations in float (weight-only: no calibration). int4: MatMul weights 4-bit (MatMulNBits, block 32), the remaining convolution weights int8 weight-only. bf16 is not made: ONNX Runtime's CUDA provider has no bf16 convolution.
+
+**Benchmark.** [DIS5K](https://github.com/xuebinqin/DIS) test sets, 250 pictures (DIS-TE1..4 and DIS-VD, 50 each), ONNX Runtime 1.30 CUDA provider on an RTX 3080 Laptop GPU. The table's last column gives the
+precision's agreement with the fp32 graph (IoU of the masks at 0.5, mean absolute difference) and its own MAE against the
+ground truth. Scored with PySODMetrics on masks shrunk to at most 2048 px on the long side (DIS5K's largest are about
+6000 x 4000); HCE (human correction effort) is given for the reference precision.
+
+| Precision | Size | vs fp32 (mask IoU, MAE) | MAE | S-measure | max F | Time / picture | Peak VRAM |
+|---|---|---|---|---|---|---|---|
+| fp32 (reference, not published) | 973 MB | - | 0.0141 | 0.953 | 0.959 | 728 ms | 9.3 GB |
+| fp16 | 490 MB | IoU 0.9981, MAE 0.0002 | 0.0141 | 0.953 | 0.959 | 583 ms | 7.7 GB |
+| int8 | 315 MB | IoU 0.9984, MAE 0.0002 | 0.0142 | 0.953 | 0.959 | 729 ms | 8.6 GB |
+| int4 | 244 MB | IoU 0.9899, MAE 0.0016 | 0.0139 | 0.953 | 0.960 | 731 ms | 8.7 GB |
+
+## How these were made
+
+From [BiRefNet](https://huggingface.co/ZhengPeng7/BiRefNet) at revision `e2bf8e4460`, starting from an fp32 ONNX graph (where it came from is said above):
+fp16 with onnxconverter-common (inputs and outputs kept fp32), int8 with ONNX Runtime static QDQ quantization
+(per-channel int8 weights, uint8 activations, MinMax calibration on pictures outside the eval set), int4 with ONNX
+Runtime's MatMulNBits (weight-only, block 32) where the graph is mostly matrix multiplications. bf16 is left out for
+convolutional graphs: ONNX Runtime's CUDA provider has no bf16 convolution. Every graph was benchmarked 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://www.sciopen.com/article/pdf/10.26599/AIR.2024.9150038.pdf'><img src='https://img.shields.io/badge/Journal-Paper-red'></a>&ensp; 
+  <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
+
+### 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', trust_remote_code=True)
+```
+
+#### Use codes from GitHub + weights from HuggingFace
+> Only use the weights on HuggingFace -- Pro: codes are always 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
+birefnet = BiRefNet.from_pretrained('ZhengPeng7/BiRefNet')
+```
+
+#### Use codes from GitHub + weights from local space
+> Only use the weights and codes both locally.
+
+```python
+# Use codes and weights locally
+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()
+
+```
+
+### 2. Use inference endpoint locally:
+> You may need to click the *deploy* and set up the endpoint by yourself, which would make some costs.
+```
+import requests
+import base64
+from io import BytesIO
+from PIL import Image
+
+
+YOUR_HF_TOKEN = 'xxx'
+API_URL = "xxx"
+headers = {
+	"Authorization": "Bearer {}".format(YOUR_HF_TOKEN)
+}
+
+def base64_to_bytes(base64_string):
+    # Remove the data URI prefix if present
+    if "data:image" in base64_string:
+        base64_string = base64_string.split(",")[1]
+
+    # Decode the Base64 string into bytes
+    image_bytes = base64.b64decode(base64_string)
+    return image_bytes
+
+def bytes_to_base64(image_bytes):
+    # Create a BytesIO object to handle the image data
+    image_stream = BytesIO(image_bytes)
+
+    # Open the image using Pillow (PIL)
+    image = Image.open(image_stream)
+    return image
+
+def query(payload):
+	response = requests.post(API_URL, headers=headers, json=payload)
+	return response.json()
+
+output = query({
+	"inputs": "https://hips.hearstapps.com/hmg-prod/images/gettyimages-1229892983-square.jpg",
+	"parameters": {}
+})
+
+output_image = bytes_to_base64(base64_to_bytes(output))
+output_image
+```
+
+
+> 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 **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 @Freepik for their generous support on GPU resources for training higher resolution BiRefNet models and more of my explorations.
++ Many thanks to @fal for their generous support on GPU resources for training better general 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}
+}
+```