This repository is dedicated to studying DETR and its subsequent research. I am implementing a smaller version of the DETR model using a toy dataset. The goal is to experiment with various components from different papers by adjusting hyperparameters.
NOTE: The average number of objects per image in this dataset is 16, which is higher than MS-COCO (7.7) or other datasets.
- Download the Safety-Helmet-Wearing-Dataset
- Before use the dataset, please check the issue#1
- DETR
- Auxiliary losses from decoder layers
- Conditional DETR
- Concatenate content and positional embeddings in the cross-attention
- DAB DETR
- Anchor query
- Modulate positional attention
- Iterative anchor update
- DN DETR
- Noised positive queries
- DNIO DETR
- A fixed number of noised queries
- Noised negative queries
- Mixed query selection
- Look forward twice
- Co DETR
- Auxiliary collaborative heads
- ATSS
- Faster-RCNN (loss calculation with ignore index)
- Multi-scale adapter
- Customized positive query
- Auxiliary collaborative heads
- Model performance (mAP)
- Check the requirements.txt.
Model training was conducted using a single A100 GPU (40GB). You can experiment with a GPU with less memory by reducing the batch size or image resolution.
# Model Training Scripts
gpu=0
ex='DETR'
python main.py --mode 'train' --device "cuda:${gpu}" --model 'detr' \
--lr 0.00005 --epochs 500 --lr_milestone 400 --return_intermediate \
--n_query 600 --decoder_ca_position_mode 'add' \
--pin_memory --save_root "model-store/${ex}" \
--pos_embedding 'sine' --temperature 10000 \
--cls_match_weight 1.0 --cls_loss_weight 1.0
ex='C_DETR'
python main.py --mode 'train' --device "cuda:${gpu}" --model 'conditional_detr' \
--epochs 50 --lr_milestone 40 --return_intermediate --n_query 600 \
--pin_memory --save_root "model-store/${ex}" \
--cls_loss 'focal' --pos_embedding 'sinev2' \
--temperature 20 --query_scale_mode 'diag'
ex='DAB-DETR'
python main.py --mode 'train' --device "cuda:${gpu}" --model 'dab-detr' \
--epochs 50 --lr_milestone 40 --return_intermediate \
--n_query 200 --num_pattern 3 \
--pin_memory --save_root "model-store/${ex}" \
--cls_loss 'focal' --pos_embedding 'sinev2' --temperature 20 \
--modulate_wh_attn --iter_update --transformer_activation 'prelu'
ex='DN-DETR'
python main.py --mode 'train' --device "cuda:${gpu}" --model 'dn-detr' \
--epochs 50 --lr_milestone 40 \
--return_intermediate --n_query 600 \
--pin_memory --save_root "model-store/${ex}" \
--cls_loss 'focal' --pos_embedding 'sinev2' --temperature 20 \
--modulate_wh_attn --iter_update --transformer_activation 'prelu' \
--num_pattern 0 --num_group 5 --box_noise_scale 0.4 --label_noise_scale 0.2
ex='DINO-DETR'
python main.py --mode 'train' --device "cuda:${gpu}" --model 'dino-detr' \
--epochs 50 --lr_milestone 40 \
--return_intermediate --n_query 300 \
--pin_memory --save_root "model-store/${ex}" \
--cls_loss 'focal' --pos_embedding 'sinev2' --temperature 20 \
--modulate_wh_attn --iter_update --transformer_activation 'prelu' \
--num_dn_query 50 --add_neg_query --num_pattern 0 --num_group 5 \
--box_noise_scale 0.4 --label_noise_scale 0.2 \
--two_stage_mode 'mix' --num_encoder_query 300 --two_stage_share_head
# Model Evaluation Scripts
ex='DETR'
save_path="model-store/${ex}/test_result/"
python main.py --mode 'test' --device "cuda:${gpu}" --pin_memory --batch_size 1 \
--eval_model_path "./model-store/${ex}/trained_model.pt" \
--save_root ${save_path}
cd mAP/
python main.py -na -np --dr "../${save_path}"
cd ..- Deformable Attention
- Multi-scale Features
- RT-DETR