keras-yolo3
Introduction
A keras implementation of YOLOv3 (Tensorflow backend) for raccoon detection (ref: qqwweee/keras-yolo3)
Raccoon dataset
Raccoon dataset is avaiable here: Raccoon dataset (modified from experiencor/raccoon_dataset)
How to use:
1) Get the model
Step 1: Download the project:
git clone https://github.com/bing0037/keras-yolo3.git
Step 2: Download YOLOv3 weights from YOLO website or yolov3.weights.
Step 3: Convert the Darknet YOLO model to a Keras model
python convert.py yolov3.cfg yolov3.weights model_data/yolo.h5 # to get yolo.h5(model)
OR download the model yolo.h5 to model_data/ directory directly.
2) Test the model on coco dataset(original yolo model is trained on coco dataset)
Run YOLO detecion.
python yolo_video.py --model_path model_data/yolo.h5 --classes_path model_data/coco_classes.txt --image
3) Retrain the model for raccoon detection:
Step 1: Download Raccoon dataset to root directory
git clone https://github.com/bing0037/Raccoon_dataset.git
Step 2: Parse annotation:
python raccoon_annotation.py
Step 3: Download YOLOv3 weights from yolo_weights to model_data/ directory
Step 4: Retrain the model(use yolo.h5 as the pretrained model)
python train.py -a Raccoon_dataset/raccoon_train_data.txt -c Raccoon_dataset/raccoon_classes.txt -o model_data/raccoon_derived_model.h5
OR download the trained model raccoon_derived_model.h5 to model_data/ directory directly.
Step 5: Run the model
python yolo_video.py --image
4) pedestrian detection: training dataset: Robust Multi-Person Tracking from Mobile Platforms
More training data is needed to improve the accuracy!
Step1: training or download the model directly pedestrian_detection_model.h5:
python train.py -a test_data/training_data/annotation.txt -c test_data/training_data/pedestrian_classes.txt -o model_data/pedestrian_detection_model.h5
Step2: running:
python yolo_video.py --model_path model_data/pedestrian_detection_model.h5 --classes_path test_data/training_data/pedestrian_classes.txt
Pedestrian detection result: Yotube
Raccoon detection result
Some issues
-
The test environment is
- Python 3.5.5
- Keras 2.2.0
- tensorflow 1.6.0
-
The model for raccoon detection was trained using ONLY CPU, so the accuracy is not very high. If you want to achieve a better performance, you can use GPUs for training.