Minimal implementation of YOLOv3 in PyTorch.

• PyTorch-YOLOv3-wildfire
  • Table of Contents
  • Installation
  • Inference
  • Video
  • Test
  • Detect
  • Train
  • Credit

$ git clone https://github.com/Wayne122/PyTorch-YOLOv3-wildfire.git
$ cd PyTorch-YOLOv3-wildfire/
$ sudo pip3 install -r requirements.txt

if you wan use pretrained darknet-53 on IMAGENET weights, please download darknet53.conv.74,and put it into checkpoints/

if you just want a pretrained weights on wildfire dataset for test or detect, please download pretrained weights file, and put it into weights folder, the path: weights/yolov3-wildfire.weights

Uses pretrained weights to make predictions on images. weights/wildfire_best.weights was trained by wildfire data set. python3 detect.py --image_folder /data/samples

rundetect.py to detect objects, and please put samples into data/samples defult weights files is weights/yolov3-wildfire.weights

run video.py to detect objects from a webcam or a video file.

run test.py

Data augmentation as well as additional training tricks remains to be implemented. PRs are welcomed!

    train.py [-h] [--epochs EPOCHS]
                [--batch_size BATCH_SIZE]
                [--model_config_path MODEL_CONFIG_PATH]
                [--data_config_path DATA_CONFIG_PATH]
                [--weights_path WEIGHTS_PATH] [--class_path CLASS_PATH]
                [--conf_thres CONF_THRES] [--nms_thres NMS_THRES]
                [--n_cpu N_CPU] [--img_size IMG_SIZE]
                [--checkpoint_interval CHECKPOINT_INTERVAL]
                [--checkpoint_dir CHECKPOINT_DIR]

@article{yolov3,
  title={YOLOv3: An Incremental Improvement},
  author={Redmon, Joseph and Farhadi, Ali},
  journal = {arXiv},
  year={2018}
}