TFCV is my repo for experimenting with Computer Vision models in Tensorflow 2. The goal is to have everything simple and searchable.
- TO-DO:
- create WiderFace dataset / TF Records using tfds
- pick apart and implement RetinaNet repo for understanding
- load backbone model from tf.keras.applications for transfer learning
- create FPN
- create context layers for classification and regression for final output
- work on anchor boxes
- image augmentations and incorporate w/ anchors for preprocessing (need to make sure crop has at least one box)
- figure out loss
- understand inference differences (non-max supression, etc.)
- modularize for tensorflow 2.2 (can now change train_step and predict_step) (currently working on)
- add callbacks
- create notebooks for each piece w/ visualizations and understanding
- run model w/ hyperparameters from RetinaFace modified for tf==2.2 and if good enough convert to tflite
- take trained model and fine tune with ArcFace loss for face embeddding
- convert that to tflite and get working on RaspberryPi
- add new backbone or object detection models
- add distributed computing
| Section | Description |
|---|---|
| Image Classification | Image classification models |
| Object Detection | Object Detection models |
| Face Verification | Face embedding methods |
| Notebooks | Step-by-step guide to implementation of the models |
| Models | Models trained using this repo |
| Literature Review | Literature review w/ links and notes |
*All other sections are code written in old version of Tensorflow that I will replace*