YOLOv3-tiny PyTorch implementation

Features

• Train network
• Test network
• Inference on an image
• Inference on a video
• Inference on a camera movie

Usage

Train

python train.py [Arguments]

[Arguments (all are optional)]

• weights : pretrained weights used when transfer learning or fine tuning (default : None)

• model : specify 'sep' if you use separable model (default : None)

• data_root : path to dataset (train.txt must be placed, default : $HOME/datasets/COCO/2014)

• num_classes : num of classification

• class_names : path to file of class name (default : namefiles/coco.names)

• output_model : file name of trained model (default : yolo-tiny.pt)

• batchsize : batch size

• lr : initial value of learning rate

• epochs : training epochs

• decay : weights decay

• trans : transfer learning switch (default : False, set to True)

• finetune : fine tuning switch (default : False, set to True)

• novalid : don't do validation after every epoch (default : False, set to True)

• nosave : don't save training result (default : False, set to True)

Run train.py like above. This training program saves the trained model into 'results/{date of end of training}/'. That directory includes model (*.pt), parameter list that have used in training (train_parameters.txt), learning rate transition (lr.png), mAP transition (mAP.png), and loss transition (loss.png).

Test

python test.py [Arguments]

[Arguments (all are optional)]

• weights : trained model (default : weights/yolov3-tiny.pt)
• model : specify 'sep' if you use separable model (default : None)
• data_root : path to dataset (test.txt must be placed, default : $HOME/datasets/COCO/2014)
• num_classes : num of classification
• class_names : path to file of class name (default : namefiles/coco.names)
• quant : quantization switch (default : False, set to True)
• nogpu : specify if you don't want to use GPU (default : False, set to True)

Inference

python detect.py [Arguments]

[Arguments (all are optional)]

• weights : trained model (default : weights/yolov3-tiny.pt)

• model : specify 'sep' if you use separable model (default : None)

• image : path to an image for inference (default : images/car.jpg)

• output_image : output file name of inference result (default : output.jpg)

• num_classes : num of classification

• class_names : path to file of class name (default : namefiles/car.names)

• quant : quantization switch (default : False, set to True)

• nogpu : specify if you don't want to use GPU (default : False, set to True)

• conf_thres : confidence threshold for NMS

• nms_thres : nms threshold for NMS (IoU threshold)

Note

• The directory 'tools/' is under development.
• Some codes are based on eriklindernoren/PyTorch-YOLOv3