Update README.md

README.md

@@ -4,6 +4,101 @@ task_categories:
 - text-to-image
 ---
 
-This repository contains the dataset described in [FaceID-6M: A Large-Scale, Open-Source FaceID Customization Dataset](https://huggingface.co/papers/2503.07091).
+# FaceID-6M: A Large-Scale, Open-Source FaceID Customization Dataset
 
-Code: https://github.com/ShuheSH/FaceID-6M
+This repository contains the dataset described in [FaceID-6M: A Large-Scale, Open-Source FaceID Customization Dataset](https://arxiv.org/abs/2503.07091).
+
+
+## Links
+- [FaceID-6M: A Large-Scale, Open-Source FaceID Customization Dataset](#faceid-6m-a-large-scale-open-source-faceid-customization-dataset)
+  - [Introduction](#introduction)
+  - [Comparison with Previous Works](#comparison-with-previous-works)
+    - [FaceID Fidelity](#faceid-fidelity)
+    - [Scaling Results](#scaling-results)
+  - [Released FaceID-6M dataset](#released-faceid-6m-dataset)
+  - [Released FaceID Customization Models](#released-faceid-customization-models)
+  - [Usage](#usage)
+  - [Contact](#contact)
+
+
+
+
+## Introduction
+
+FaceID-6M, is the first large-scale, open-source faceID dataset containing 6 million high-quality text-image pairs. Filtered from [LAION-5B](https://laion.ai/blog/laion-5b/), which includes billions of diverse and publicly available text-image pairs, FaceID-6M undergoes a rigorous image and text filtering process to ensure dataset quality. For image filtering, we apply a pre-trained face detection model to remove images that lack human faces, contain more than three faces, have low resolution, or feature faces occupying less than 4% of the total image area. For text filtering, we implement a keyword-based strategy to retain descriptions containing human-related terms, including references to people (e.g., man), nationality (e.g., Chinese), ethnicity (e.g., East Asian), professions (e.g., engineer), and names (e.g., Donald Trump). 
+Through these cleaning processes, FaceID-6M provides a high-quality dataset optimized for training powerful FaceID customization models, facilitating advancements in the field by offering an open resource for research and development.
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/60e3bcb1d2fcc718c610a996/HnVTsPkIGFDAT6wAzC18P.png)
+
+
+
+## Comparison with Previous Works
+
+
+### FaceID Fidelity
+
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/60e3bcb1d2fcc718c610a996/0nAK6GvUj68-CgSUNXEWW.png)
+
+Based on these results, we can infer that the model trained on our FaceID-6M dataset achieves a level of performance comparable to the official InstantID model in maintaining FaceID fidelity. For example, in case 2 and case 3, both the official InstantID model and the FaceID-6M-trained model effectively generate the intended images based on the input. 
+This clearly highlights the effectiveness of our FaceID-6M dataset in training robust FaceID customization models.
+
+
+### Scaling Results
+
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/60e3bcb1d2fcc718c610a996/L3RjSgXb0kyM9UuPV_eGI.png)
+
+To evaluate the impact of dataset size on model performance and optimize the trade-off between performance and training cost, we conduct scaling experiments by sampling subsets of different sizes from FaceID-6M. 
+The sampled dataset sizes include: (1) 1K, (2) 10K, (3) 100K, (4) 1M, (5) 2M, (6) 4M, and (7) the full dataset (6M).
+For the experimental setup, we utilize the [InstantID](https://github.com/instantX-research/InstantID) FaceID customization framework and adhere to the configurations used in the previous quantitative evaluations. The trained models are tested on the subset of [COCO2017](https://cocodataset.org/#detection-2017) test set, with Face Sim, CLIP-T, and CLIP-I as the evaluation metrics.
+
+
+The results demonstrate a clear correlation between training dataset size and the performance of FaceID customization models.
+For example, the Face Sim score increased from 0.38 with 2M training data, to 0.51 with 4M, and further improved to 0.63 when using 6M data.
+These results underscore the significant contribution of our FaceID-6M dataset in advancing FaceID customization research, highlighting its importance in driving improvements in the field.
+
+
+## Released FaceID-6M dataset
+
+We release two versions of our constructed dataset:
+1. [FaceID-70K](https://huggingface.co/datasets/Super-shuhe/FaceID-70K): This is a subset of our FaceID-6M by further removing images lower than 1024 pixels either in width or height, consisting approximately 70K text-image pairs. 
+2. [FaceID-6M](https://huggingface.co/datasets/Super-shuhe/FaceID-6M): This is our constructed full FaceID customization dataset.
+
+Please note that due to the large file size, we have pre-split it into multiple smaller parts. Before use, please execute the merge and unzip commands to restore the full file. Take the InstantID-FaceID-70K dataset as the example:
+1. `cat laion_1024.tar.gz.* > laion_1024.tar.gz`
+2. `tar zxvf laion_1024.tar.gz`
+
+**Index**
+
+After restoring the full dataset, you will find large amounts `.png` and `.npy` file, and also a `./face` directory and a `*.jsonl` file:
+1. `*.png`: Tmage files
+2. `*.npy`: The pre-computed landmarks of the face in the related image, which is necessary to train [InstantID-based models](https://instantid.github.io/). If you don't need that, just ignore them.
+3. `./face`: The directory including face files.
+4. `*.jsonl`: Descriptions or texts. Ignore the file paths listed in the `.jsonl` file and use the line number instead to locate the corresponding image, face, and `.npy` files. For example, the 0th line in the `.jsonl` file corresponds to `0.png`, `0.npy`, and `./face/0.png`.
+
+
+## Released FaceID Customization Models
+
+We release two versions of trained InstantID models:
+1. [InstantID-FaceID-70K](https://huggingface.co/Super-shuhe/InstantID-FaceID-70K): Model trained on our [FaceID-70K](https://huggingface.co/datasets/Super-shuhe/FaceID-70K) dataset.
+2. [InstantID-FaceID-6M](https://huggingface.co/Super-shuhe/InstantID-FaceID-6M): Model trained on our [FaceID-6M](https://huggingface.co/datasets/Super-shuhe/FaceID-6M) dataset.
+
+
+## Usage
+
+For instructions on training and inference of FaceID customization models using our dataset, please visit our GitHub repository: https://github.com/ShuheSH/FaceID-6M
+
+
+## Contact
+
+If you have any issues or questions about this repo, feel free to contact [email protected]
+
+```
+@article{wang2025faceid,
+  title={FaceID-6M: A Large-Scale, Open-Source FaceID Customization Dataset},
+  author={Wang, Shuhe and Li, Xiaoya and Li, Jiwei and Wang, Guoyin and Sun, Xiaofei and Zhu, Bob and Qiu, Han and Yu, Mo and Shen, Shengjie and Hovy, Eduard},
+  journal={arXiv preprint arXiv:2503.07091},
+  year={2025}
+}
+```