${ROOT}   
└── dataset/    
    └── kitti
        ├──ImageSets/
        |   ├── train.txt
        |   ├── val.txt
        ├── training/
        |   ├── image_2/ <-- for visualization
        |   ├── calib/
        |   ├── label_2/
        |   ├── velodyne/
        └── testing/  
        |   ├── image_2/ <-- for visualization
        |   ├── calib/
        |   ├── velodyne/ 
└── src/
    └── config/
    └── data_process/
    └── models/
    └── utils/
    └── demo.py
    └── eval_mAP.py
    └── test.py
    └── train.py
    └── train.sh
├── README.md 
├── requirements.txt

usage: train.py [-h] [--seed SEED] [--saved_fn FN] [-a ARCH] [--cfgfile PATH]
                [--pretrained_path PATH] [--img_size IMG_SIZE]
                [--multiscale_training] [--no-val] [--num_samples NUM_SAMPLES]
                [--num_workers NUM_WORKERS] [--batch_size BATCH_SIZE]
                [--subdivisions SUBDIVISIONS] [--print_freq N]
                [--tensorboard_freq N] [--checkpoint_freq N] [--start_epoch N]
                [--num_epochs N] [--lr LR] [--minimum_lr MIN_LR]
                [--momentum M] [-wd WD] [--optimizer_type OPTIMIZER]
                [--lr_type SCHEDULER] [--burn_in N]
                [--steps [STEPS [STEPS ...]]] [--world-size N] [--rank N]
                [--dist-url DIST_URL] [--dist-backend DIST_BACKEND]
                [--gpu_idx GPU_IDX] [--no_cuda]
                [--multiprocessing-distributed] [--evaluate]
                [--resume_path PATH]

The Implementation of Complex YOLOv4

optional arguments:
  -h, --help            show this help message and exit
  --seed SEED           re-produce the results with seed random
  --saved_fn FN         The name using for saving logs, models,...
  -a ARCH, --arch ARCH  The name of the model architecture
  --cfgfile PATH        The path for cfgfile (only for darknet)
  --pretrained_path PATH
                        the path of the pretrained checkpoint
  --img_size IMG_SIZE   the size of input image
  --multiscale_training
                        If true, use scaling data for training
  --no-val              If true, dont evaluate the model on the val set
  --num_samples NUM_SAMPLES
                        Take a subset of the dataset to run and debug
  --num_workers NUM_WORKERS
                        Number of threads for loading data
  --batch_size BATCH_SIZE
                        mini-batch size (default: 64), this is the totalbatch
                        size of all GPUs on the current node when usingData
                        Parallel or Distributed Data Parallel
  --subdivisions SUBDIVISIONS
                        subdivisions during training
  --print_freq N        print frequency (default: 10)
  --tensorboard_freq N  frequency of saving tensorboard (default: 10)
  --checkpoint_freq N   frequency of saving checkpoints (default: 3)
  --start_epoch N       the starting epoch
  --num_epochs N        number of total epochs to run
  --lr LR               initial learning rate
  --minimum_lr MIN_LR   minimum learning rate during training
  --momentum M          momentum
  -wd WD, --weight_decay WD
                        weight decay (default: 1e-6)
  --optimizer_type OPTIMIZER
                        the type of optimizer, it can be sgd or adam
  --lr_type SCHEDULER   the type of the learning rate scheduler (steplr or
                        ReduceonPlateau)
  --burn_in N           number of burn in step
  --steps [STEPS [STEPS ...]]
                        number of burn in step
  --world-size N        number of nodes for distributed training
  --rank N              node rank for distributed training
  --dist-url DIST_URL   url used to set up distributed training
  --dist-backend DIST_BACKEND
                        distributed backend
  --gpu_idx GPU_IDX     GPU index to use.
  --no_cuda             If true, cuda is not used.
  --multiprocessing-distributed
                        Use multi-processing distributed training to launch N
                        processes per node, which has N GPUs. This is the
                        fastest way to use PyTorch for either single node or
                        multi node data parallel training
  --evaluate            only evaluate the model, not training
  --resume_path PATH    the path of the resumed checkpoint