Detecting Every Object from Events

Framework

Comparasion with the RVT in close-set (pedestrians and cars) setting

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Installation

We recommend using cuda11.8 to avoid unnecessary environmental problems.

conda create -y -n deoe python=3.11
conda activate deoe

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

pip install wandb pandas plotly opencv-python tabulate pycocotools bbox-visualizer StrEnum hydra-core einops 
torchdata tqdm numba h5py hdf5plugin lovely-tensors tensorboardX pykeops scikit-learn ipdb timm opencv-python-headless
pytorch_lightning==1.8.6 numpy==1.26.3

Required Data

We recommend using DSEC-Detection training and evaluation first (about 2 days), since 1 Mpx usually takes a long time to train (about 10 days) if you only have a single GPU.

DSEC

You can download the processed DSEC-Detection by clicking here.

GEN4

You can get the raw GEN4 in RVT. And get the processed data by following the Instruction proposed by RVT. Note that to keep the labels for all the classes following here.

Checkpoints

	DSEC-Detection	GEN4
Pre-trained checkpoints	download	download
AUC-Unknown	25.1	23.5

Evaluation

Set DATASET = dsec or gen4.

Set DATADIR = path to the DSEC-Detection or 1 Mpx dataset directory.

Set CHECKPOINT = path to the checkpoint used for evaluation.

python validation.py dataset={DATASET} dataset.path={DATADIR} checkpoint={CHECKPOINT} +experiment/{DATASET}='base.yaml'

The batchsize, lr, and the other hyperparameters could be adjusted in file config\experiments\dataset\base.yaml.

Evaluation for mixed categories or each category.

Set the testing_classes to full categories in file config\dataset\dataset.yaml.

Set the unseen_classes to the categories evaluated as the unknown categories in file config\dataset\dataset.yaml.

The first results outpute by the console are the results for unseen classes, while the second is for testing classes (generally full categories).

Computed AUC for recall curve.

python compute_auc.py

Training

Set DATASET = dsec or gen4.

Set DATADIR = path to the DSEC-Detection or 1 Mpx dataset directory.

python train.py dataset={DATASET} dataset.path={DATADIR} +experiment/{DATASET}='base.yaml'

The batchsize, lr, and the other hyperparameters could be adjusted in file config\experiments\dataset\base.yaml.

Visualization of results

Set DATASET = dsec or gen4.

Set CHECKPOINT = path to the checkpoint used for evaluation.

Set h5_file = path to files used for visualization like h5_file = /DSEC_process/val/zurich_city_15_a.

python demo.py dataset={DATASET} checkpoint={CHECKPOINT} +experiment/{DATASET}='base.yaml'

Then the output images and video will be saved in folder DEOE\prediction.

Citation

If you find our work is helpful, please considering cite us.

@article{zhang2024detecting,
  title={Detecting Every Object from Events},
  author={Zhang, Haitian and Xu, Chang and Wang, Xinya and Liu, Bingde and Hua, Guang and Yu, Lei and Yang, Wen},
  journal={arXiv preprint arXiv:2404.05285},
  year={2024}
}