App/LicensePlates | ||
font | ||
Images | ||
img | ||
model_data | ||
temp | ||
test | ||
train | ||
valid | ||
yolo3 | ||
api.py | ||
convert.py | ||
darknet53.cfg | ||
ocr.py | ||
README.md | ||
train_bottleneck.py | ||
train.py | ||
trained_weights_final.h5 | ||
voc_annotation.py | ||
yolo_video.py | ||
yolo.ipynb | ||
yolo.py | ||
yolov3-tiny.cfg | ||
yolov3.cfg | ||
yolov3.weights |
keras-yolo3 with Roboflow
A Keras implementation of YOLOv3 (Tensorflow backend) inspired by allanzelener/YAD2K.
What You Will Learn
- How to load your custom image detection data from Roboflow
- How set up the YOLOv3 model in keras
- How to train the YOLOv3 model
- How to use the model for inference
- How to save the keras model weights for future use
Resources
- This blog post provides a deep dive into the tutorial
- This notebook provides the code necessary to run the tutorial
- For reading purposes, the notebook is also saved in Tutorial.ipynb
About Roboflow for Data Management
Roboflow makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless. Developers reduce 50% of their code when using Roboflow's workflow, automate annotation quality assurance, save training time, and increase model reproducibility.