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---
license: apache-2.0
datasets:
- ILSVRC/imagenet-1k
language:
- en
metrics:
- accuracy
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}")
``` |