Update README.md

README.md

@@ -9,218 +9,5 @@ tags:
 - pytorch_model_hub_mixin
 - model_hub_mixin
 - transformers
-repo_url: https://github.com/ZhengPeng7/BiRefNet
 pipeline_tag: image-segmentation
-license: mit
 ---
-<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}
-}
-```