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genview_pretrained_models

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metadata
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 (ECCV 2024)

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
  2. Evaluation
  3. Citation
  4. 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

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:

@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 

# Replace {MODEL_FILE} with the specific model file name
wget https://huggingface.co./Xiaojie0903/genview_pretrained_models/resolve/main/{MODEL_FILE}

Example:

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 

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}")