- Implementation crowd-counting to traffic domain and counting vehicle on road
- paper reference: CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes
- torch
- torchvision
- pillow
- opencv-python
- numpy
- GPU-CUDA (optional)
python3 train.py --train <path to folder training set> -cfg model.json --use_pretrain --density <path to dataset density> --cuda -i 200 -lr 0.000001 -wk 3 -bs 4
--cuda: flag to use GPU NVIDIA to training phase. If you don't want use GPUs just skip it.
-wk:(worker) multi-processor to train, it make training phase more faster( if you got strong cpu like i5, ryzen5, xeon, ...)
-bs: batch-size
-lr: learning rate, make it small enough to training, can you monitor with tensorboard and watch total gradient graph it must be fluctuate
python3 train.py --train <path to folder training set> -cfg model.json --density <path to dataset density> --cuda -i 200 -lr 0.000001 -wk 3 -bs 4 --checkpoint
python3 eval.py --test <path to folder training set> -cfg model.json --density <path to eval density> --cuda -wk 3 -bs 4
python3 video_test.py --input <path to video> -cfg model.json --cuda -l ./logs