Awesome

LMC: Large Model Collaboration with Cross-assessment for Training-Free Open-Set Object Recognition, NeurIPS 2023

¹Haoxuan Qu*, ¹Xiaofei Hui*, ²Yujun Cai, ¹Jun Liu,

* equal contribution

¹Singapore University of Technology and Design, ²Meta

[Paper] | [Arxiv] | [SUTD-VLG Lab]

Overview of LMC

Environment

The code is developed and tested under the following environment:

• Python 3.9
• PyTorch 2.0.0
• CUDA 11.7

You can create the environment via:

conda create -n lmc python=3.9
conda activate lmc
conda install pytorch==2.0.0 torchvision==0.15.0 torchaudio==2.0.0 pytorch-cuda=11.7 -c pytorch -c nvidia
pip install ftfy regex tqdm scikit-learn scipy pandas six timm
pip install transformers openai
pip install git+https://github.com/openai/CLIP.git

Preparation

Dataset

Tinyimagenet can be downloaded by running:

cd data
bash tinyimagenet.sh

Pre-trained model

To load CLIP pre-trained weights, you can visit official CLIP GitHub Repo and download CLIP "ViT-B/32" to pretrained_model using download address in this page.

To load DINO pre-trained weights, you can visit official DINOv2 and download "ViT-B/14 distilled" to pretrained_model using download address in this page.

Evaluation

To evaluate our framework (e.g., on Tinyimagenet) using our provided virtual open-set classes, please unzip tiny_img.zip and run:

python tinyimagenet_eval_msp.py --save_dir path\to\save\result --image_path path\to\unzipped\images

Note that here we provide virtual open-set classes and generated images that can yield slightly better results than the results provided in our paper for Tinyimagenet.

Acknowledgement

Part of our code is borrowed from ZOC. We thank the authors for releasing the codes.