School projects in the Object Recognition and Computer Vision Course (I. LAPTEV, J. PONCE, C. SCHMID, J. SIVIC) shared between Master 2 MASH in Paris Dauphine University and Master 2 MVA in ENS
MVA Kaggle Competition : Bird Classification (https://www.kaggle.com/competitions/mva-recvis-2022), Our solution got 84% Accuracy on the final test set. Download the training/validation/test images from here. The test image labels are not provided.
Run the file crop.py
Run The file augementation.py
Run main.py
Run eval.py
We studied DiffusionDet which is a diffusion model for object detection. Specifically, we reproduced the results of the author on MS-COCO dataset and compared DiffusionDet performances with Faster R-CNN. The main goal was to extend the use of Diffusion model to Multi-Object Tracking. We implemented a centroid-based tracker on top of the DiffusionDet model.
Compressed.mp4
We have used the MS-COCO dataset for our object detection experiments and the MOT17 dataset for our Multi-Object Tracking experiments.
Chen & al., DiffusionDet : Diffusion Model for Object Detection (arxiv)
@misc{https://doi.org/10.48550/arxiv.2211.09788,
doi = {10.48550/ARXIV.2211.09788},
url = {https://arxiv.org/abs/2211.09788},
author = {Chen, Shoufa and Sun, Peize and Song, Yibing and Luo, Ping},
keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {DiffusionDet: Diffusion Model for Object Detection},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}