Update README.md

README.md

@@ -42,6 +42,128 @@ configs:
 [![HuggingFace](https://img.shields.io/badge/HuggingFace-Model-yellow.svg)](https://huggingface.co/WithAnyone/WithAnyone)
 [![MultiID-Bench](https://img.shields.io/badge/MultiID-Bench-Green.svg)](https://huggingface.co/datasets/WithAnyone/MultiID-Bench)
 [![MultiID-2M](https://img.shields.io/badge/MultiID_2M-Dataset-Green.svg)](https://huggingface.co/datasets/WithAnyone/MultiID-2M)
-[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://www.apache.org/licenses/LICENSE-2.0)
+[![Demo](https://img.shields.io/badge/HuggingFace-Demo-Yellow.svg)](https://huggingface.co/spaces/WithAnyone/WithAnyone_demo)
+
+**Please refer to [GitHub repo](https://github.com/Doby-Xu/WithAnyone) for the usage of this benchmark.**
+
+## Download
+
+[HuggingFace Dataset](https://huggingface.co/datasets/WithAnyone/MultiID-Bench)
+
+```
+huggingface-cli download WithAnyone/MultiID-Bench --repo-type dataset --local-dir <path to MultiID-Bench directory>
+```
+
+## Evaluation
+
+**Please refer to [GitHub repo](https://github.com/Doby-Xu/WithAnyone) for the usage of this benchmark.**
+
+### Environment Setup
+
+Besides the `requirements.txt` in [GitHub repo](https://github.com/Doby-Xu/WithAnyone), you need to install the following packages:
+
+```bash
+pip install aesthetic-predictor-v2-5 
+pip install facexlib
+pip install colorama
+pip install pytorch_lightning
+git clone https://github.com/timesler/facenet-pytorch.git facenet_pytorch
+
+# in MultiID_Bench/
+mkdir pretrained
+```
+
+
+You need the following models to run the evaluation:
+
+- CLIP
+- arcface
+- aesthetic-v2.5
+- adaface
+- facenet
+
+For the first three models, they will be automatically downloaded when you run the evaluation script for the first time. Most of the models will be cached in the `HF_HOME` directory, which is usually `~/.cache/huggingface`. About 5GB of disk space is needed.
+
+For adaface, you need to download the model weights from [adaface_ir50_ms1mv2.ckpt](https://drive.google.com/file/d/1eUaSHG4pGlIZK7hBkqjyp2fc2epKoBvI/view?usp=sharing) (This is the original link provided by the authors of AdaFace) and put it in the `pretrained` directory.
+
+This repository includes code from [AdaFace](https://github.com/mk-minchul/AdaFace?tab=readme-ov-file). AdaFace is included in this codebase for merely easier import. You can also clone it separately from its original repository, and modify the import paths accordingly.
+
+
+### Data to Evaluate
+
+By running:
+```
+python hf2bench.py \
+    --dataset WithAnyone/MultiID-Bench \
+    --output <root directory to save the data> \
+    --from_hub
+```
+you can arrange the generated images and the corresponding text prompts in the following structure:
+```
+root/
+├── id1/
+│   ├── out.jpg
+│   ├── ori.jpg
+│   ├── ref_1.jpg
+│   ├── ref_2.jpg
+│   ├── ref_3.jpg
+│   ├── ref_4.jpg
+│   └── meta.json
+│
+├── id2/
+│   ├── out.jpg
+│   ├── ori.jpg
+│   ├── ref_1.jpg
+│   ├── ref_2.jpg
+│   ├── ref_3.jpg
+│   ├── ref_4.jpg
+│   └── meta.json
+│
+└── ...
+``` 
+
+Or you can manually download the data by
+```
+huggingface-cli download WithAnyone/MultiID-Bench --repo-type dataset --local-dir <root directory to save the data>
+```
+and arrange the files:
+```
+python hf2bench.py --dataset <root directory to save the data> --output <root directory to save the data>
+```
+
+If you run the `infer_withanyone.py` script in this repository, the output directory will be in the correct format.
+
+The `meta.json` file should contain the prompt used to generate the image, in the following format:
+
+```json
+{
+    "prompt": "a photo of a person with blue hair and glasses"
+}
+```
+
+### Run Evaluation
+
+You can run the evaluation script as follows:
+
+```python
+from eval import BenchEval_Geo
+
+def run():
+    evaler = BenchEval_Geo(
+        target_dir=<root directory mentioned above>,
+        output_dir=<output directory to save the evaluation results>,
+        ori_file_name="ori.jpg", # the name of the ground truth image file
+        output_file_name="out.jpg", # the name of the generated image file
+        ref_1_file_name="ref_1.jpg", # the name of the first reference image file
+        ref_2_file_name="ref_2.jpg", # the name of the second reference image file
+        # ref_2_file_name=None, # if you only have one reference image, set ref_2_file_name to None
+        # ref_3_file_name="ref_3.jpg", # the name of the third reference
+        # ref_4_file_name="ref_4.jpg", # the name of the fourth reference,
+        caption_keyword="prompt", # the keyword to extract the prompt from meta.json
+        names_keyword=None
+    )
+    evaler()
+if __name__ == "__main__":
+    run()
+```
 
-Please refer to [GitHub repo](https://github.com/Doby-Xu/WithAnyone) for the usage of this benchmark.