[ Models ] & [ Path ] to be downloaded from Google Drive
- Dataset & a pickle dump of the preprocessed data (Please download all the files in this path and place them in the project root path)
- ./dataset/
- Pretrained weights on COCO dataset
- ./yolo/data/yolov4_coco_pretrained_weights.pth
- Weights of our best YOLO model (mAP 63.47%)
- ./yolo/data/yolov4_test.pth
- vgg 16 model (for OpenCV demo)
- ./vgg.pth
- Before you run the Transfer_learning.ipynb, please make sure to download the dataset folder from the google drive and put it in the project root path
- You can either use the vgg model from the google drive to run the demo, or run the Transfer_learning.ipynb and save a model (Resnet 50 or vgg 16).
- ./yolo/
- Main structure derives from reference project [1]
- ./yolo/annotation_preprocess.ipynb
- Make annotation files for training YOLO models.
- ./yolo/train.ipynb
- Train a YOLO model.
- ./yolo/predict.ipynb
- Detect images or videos (based on yolo.py in the folder).
- ./yolo/eval.ipynb
- Evaluate a YOLO model (generate ground-truth files and detection-results files).
- ./yolo/yolo.py
- Use the given YOLO model to make predictions.
- You may want to change the model weights path in this file to try out different models.
- ./yolo/data
- Store train / val / test dataset annotations.
- Store classes and anchors data.
- Store font file for detection results labeling.
- ./yolo/loss_data
- Store training losses
- ./yolo/map
- Code for computing mAP (please run main.py after running eval.ipynb) [2]
- ./yolo/nets/
- YOLO V4 implementation [1]
- ./yolo/utils/
- Utility code