Update README.md

README.md

@@ -6,5 +6,108 @@ language:
 - en
 metrics:
 - accuracy
-pipeline_tag: feature-extraction
----
+pipeline_tag: image-classification
+---
+
+# **GenView Pretrained Models**
+
+## Model Name
+**GenView: Enhancing View Quality with Pretrained Generative Models**
+
+### Summary
+This repository hosts pretrained models developed as part of the GenView framework, introduced in the ECCV 2024 paper *GenView: Enhancing View Quality with Pretrained Generative Model for Self-Supervised Learning*. These models are designed for visual representation tasks, including image classification, multimodal learning, and feature extraction. GenView leverages generative models to enhance self-supervised learning by improving view quality and diversity.
+
+---
+
+## Table of Contents
+1. [Model Details](#model-details)
+2. [Evaluation](#evaluation)
+3. [Citation](#citation)
+4. [How to Download the Model](#how-to-download-the-model)
+
+---
+
+## Model Details
+
+### **Model Description**
+The GenView pretrained models include both convolutional architectures (e.g., ResNet50) and transformer-based architectures (e.g., ViT-B). These models utilize advanced self-supervised learning methods such as SimSiam, MoCo, and BYOL. By incorporating generative models for adaptive view generation, the framework delivers superior feature representations.
+
+- **Developed by:** Xiaojie Li, Yibo Yang, Xiangtai Li, Jianlong Wu, Yue Yu, Bernard Ghanem, Min Zhang  
+- **Funded by:** Harbin Institute of Technology, Shenzhen; Peng Cheng Laboratory; KAUST; NTU  
+- **Shared by:** Xiaojie Li  
+- **Model type:** Self-supervised learning for vision tasks  
+- **Language:** Vision-focused (not language-specific)  
+- **License:** Apache 2.0  
+
+### **Model Sources**
+- **Hugging Face Repository:** [GenView Pretrained Models](https://huggingface.co/Xiaojie0903/genview_pretrained_models)  
+- **GitHub Repository:** [GenView Official Code](https://github.com/xiaojieli0903/genview/)  
+- **Paper:** [GenView: Enhancing View Quality with Pretrained Generative Model for Self-Supervised Learning (ECCV 2024)](https://arxiv.org/abs/2403.12003)  
+
+---
+
+## Evaluation
+
+### **Testing Data**
+Linear Probe evaluation was conducted using the ImageNet-1K dataset.
+
+### **Metrics**
+The models were evaluated based on Top-1 accuracy.
+
+### **Results**
+
+| Method           | Backbone     | Pretraining Epochs | Linear Probe Accuracy (%) |
+|-------------------|--------------|---------------------|----------------------------|
+| MoCo v2 + GenView| ResNet-50    | 200                 | 70.0                       |
+| SwAV + GenView   | ResNet-50    | 200                 | 71.7                       |
+| SimSiam + GenView| ResNet-50    | 200                 | 72.2                       |
+| BYOL + GenView   | ResNet-50    | 200                 | 73.2                       |
+| MoCo v3 + GenView| ResNet-50    | 100                 | 72.7                       |
+| MoCo v3 + GenView| ResNet-50    | 300                 | 74.8                       |
+| MoCo v3 + GenView| ViT-S        | 300                 | 74.5                       |
+| MoCo v3 + GenView| ViT-B        | 300                 | 77.8                       |
+
+---
+
+## Citation
+
+If you use these models, please cite the GenView paper:
+
+```bibtex
+@inproceedings{li2023genview,
+  author={Li, Xiaojie and Yang, Yibo and Li, Xiangtai and Wu, Jianlong and Yu, Yue and Ghanem, Bernard and Zhang, Min},
+  title={GenView: Enhancing View Quality with Pretrained Generative Model for Self-Supervised Learning}, 
+  year={2024},
+  booktitle={Proceedings of the European Conference on Computer Vision},
+  pages={306--325},
+  publisher="Springer"
+}
+```
+---
+## How to Download the Model
+
+### **Downloading Models**
+To download models, use the following commands:
+
+#### Option 1: `wget`
+```bash
+# Replace {MODEL_FILE} with the specific model file name
+wget https://huggingface.co/Xiaojie0903/genview_pretrained_models/resolve/main/{MODEL_FILE}
+```
+
+Example:
+```bash
+wget https://huggingface.co/Xiaojie0903/genview_pretrained_models/resolve/main/mocov3_resnet50_8xb512-amp-coslr-100e_in1k_genview.pth
+```
+
+#### Option 2: Hugging Face Python API
+```python
+from huggingface_hub import hf_hub_download
+
+# Replace with your desired model file
+file_path = hf_hub_download(
+    repo_id="Xiaojie0903/genview_pretrained_models",
+    filename="mocov3_resnet50_8xb512-amp-coslr-100e_in1k_genview.pth"
+)
+print(f"Model downloaded to {file_path}")
+```