| .. | ||
| font | ||
| Images | ||
| model_data | ||
| test | ||
| train | ||
| valid | ||
| yolo3 | ||
| api.py | ||
| convert.py | ||
| darknet53.cfg | ||
| ocr.py | ||
| README.md | ||
| train_bottleneck.py | ||
| train.py | ||
| voc_annotation.py | ||
| yolo_video.py | ||
| yolo.py | ||
| 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.
