This work builds on tf-eager-fasterrcnn
Faster R-CNN R-101-FPN model was implemented with TensorFlow2.0 Eager Execution.
Cascade RCNN model was implemented with TensorFlow2.0 Eager Execution.
- Cuda 10.0
- Python 3.5
- TensorFlow 2.0.0
- cv2
see train_cascade_rcnn.ipynb, train_faster_rcnn.ipynb
, inspect_model.ipynb
and eval_model.ipynb
Make your directory as follow.
COCO2017
--tmp_xml
--labelimg2coco.py
And put your images and xml files in tmp_xml.
Then just run labelimg2coco.py.
- Muti-Scaling Training
- Pseudo Labeling
- GHM-C loss
- GHM-R loss
- Statistic Analysis of Dataset
- WBF
- TTA
- CutOut
- MixUp
- Dilated Conv
- SAG
This work builds on many excellent works, which include: