Update README to lite version.

README.md

@@ -3,9 +3,7 @@ library_name: BiRefNet
 tags:
 - background-removal
 - mask-generation
-- Dichotomous Image Segmentation
-- Camouflaged Object Detection
-- Salient Object Detection
+- Image Matting
 - pytorch_model_hub_mixin
 - model_hub_mixin
 repo_url: https://github.com/ZhengPeng7/BiRefNet-portrait
@@ -34,132 +32,37 @@ pipeline_tag: image-segmentation
   <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-portrait'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20HF%20Models-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 holds the official weights of BiRefNet for general matting.
 
-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___). 
+### Training Sets:
++ P3M-10k (except TE-P3M-500-P)
++ [TR-humans](https://huggingface.co/datasets/schirrmacher/humans)
 
-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
+### Validation Sets:
++ TE-P3M-500-P
 
-### 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-portrait', 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-portrait')
-```
-
-#### 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()
-
-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')
-
-    # 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 :)
+### Performance:
+|    Dataset    |            Method           | Smeasure | maxFm | meanEm | MAE  | maxEm | meanFm | wFmeasure | adpEm | adpFm | HCE |
+|     :------:    | :------: |  :------: |  :------: |  :------: |  :------: |  :------: |  :------: |  :------: |  :------: |  :------: |  :------: |
+| TE-P3M-500-P | BiRefNet-portrai--epoch_150 |  .983   |  .996 |  .991  | .006 |  .997 |  .988  |    .990   |  .933 |  .965 | .000   |
 
 
-#### Try our online demos for inference:
+**Check the main BiRefNet model repo for more info and how to use it:**  
+https://huggingface.co/ZhengPeng7/BiRefNet/blob/main/README.md
 
-+ 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" />
+**Also check the GitHub repo of BiRefNet for all things you may want:**  
+https://github.com/ZhengPeng7/BiRefNet
 
 ## 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.
++ Many thanks to @fal for their generous support on GPU resources for training this BiRefNet for portrait matting.
 
 
 ## Citation