SRFS- Transformer

Environment

python ==3.6
pytorch ==1.80
opencv-python
scipy
h5py
pillow
imageio
nni
mmcv
tensorboard

Prepare data

Generate point map

cd CLTR/data
For JHU-Crowd++ dataset: python prepare_jhu.py --data_path /xxx/xxx/jhu_crowd_v2.0
For NWPU-Crowd dataset: python prepare_nwpu.py --data_path /xxx/xxx/NWPU_CLTR

Generate image list

cd CLTR
python make_npydata.py --jhu_path /xxx/xxx/jhu_crowd_v2.0 --nwpu_path /xxx/xxx/NWPU_CLTR

Training

Example (some hyper-parameters may be different from the original paper):
sh experiments/jhu.sh
or
sh experiments/nwpu.sh

• Please change nproc_per_node and gpu_id of jhu.sh/nwpu.sh, if you do not have enogh GPU.
• We have fixed all random seeds, i.e., different runs will report the same results under the same setting.
• The model will be saved in CLTR/save_file/log_file
• Note that using FPN will improve the performance, but we do not add it in this version.
• Turning some hyper-parameters will also bring improvement (e.g., the image size, crop size, number of queries).

Testing

Example:
python test.py --dataset jhu --pre model.pth --gpu_id 2,3
or
python test.py --dataset nwpu --pre model.pth --gpu_id 0,1

• The model.pth can be obtained from the training phase.