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LLA is the first one-stage detector that surpasses two-stage detectors (e.g., Faster R-CNN) on CrowdHuman dataset. Experiments in the paper were conducted on the internal framework, thus we reimplement them on cvpods and report details as below.
python3 -m pip install 'git+https://github.com/Megvii-BaseDetection/cvpods.git'# (add --user if you don't have permission)# Or, to install it from a local clone:
git clone https://github.com/Megvii-BaseDetection/cvpods.git
python3 -m pip install -e cvpods
# Or,
pip install -r requirements.txt
python3 setup.py build develop
prepare datasets
cd /path/to/cvpods/datasets
ln -s /path/to/your/crowdhuman/dataset crowdhuman
Train & Test
git clone https://github.com/Megvii-BaseDetection/LLA.git
cd LLA/playground/detection/crowdhuman/lla.res50.fpn.crowdhuman.800size.30k # for example# Train
pods_train --num-gpus 8
# Test
pods_test --num-gpus 8 \
MODEL.WEIGHTS /path/to/your/save_dir/ckpt.pth # optional
OUTPUT_DIR /path/to/your/save_dir # optional# Multi node training## sudo apt install net-tools ifconfig
pods_train --num-gpus 8 --num-machines N --machine-rank 0/1/.../N-1 --dist-url "tcp://MASTER_IP:port"