Global-wheat-detection

Global wheat detection using YOLOv2 in Keras.

This is a Keras implementation of YOLOV2 for Kaggle Competition https://www.kaggle.com/c/global-wheat-detection/

Setup

• Run the command to install all required packages pip install -r requirements.txt

Train

1. Data preparation

Download the Global-wheat-detection dataset from from https://www.kaggle.com/c/global-wheat-detection/.

Organize the dataset into 4 folders:

• train_image_folder <= the folder that contains the train images.

• train_annot_folder <= the folder that contains the train annotations in VOC format.

• valid_image_folder <= the folder that contains the validation images.

• valid_annot_folder <= the folder that contains the validation annotations in VOC format.

There is a one-to-one correspondence by file name between images and annotations. If the validation set is empty, the training set will be automatically splitted into the training set and validation set using the ratio of 0.8.

2. Convert Bounding box into VOC format for training

Run the jupyter notebook preproc.ipynb to create VOC format xml files.

3. Download the Yolov2 weights

Download the yolov2 weights from https://pjreddie.com/darknet/yolov2/ and save it in model_weights folder. Here we are using transfer learning to train wheat detection model.

4. Train

Use the train.ipynb to train the model.

5. Inference

Use the Inference.ipynb for prediction on test dataset.

Refereneces

• https://github.com/experiencor/keras-yolo2
• https://www.kaggle.com/c/global-wheat-detection/
• https://pjreddie.com/darknet/yolov2/