This project applies the RetinaNet object detector on the GDXray dataset, Castings group.
We entirely reuse the Keras Retinanet Object Detection Framework and just apply the dataset as instructed by the framework.
We use the frameworks's default Focal Loss hyper-paramters of alpha=0.25
, and gamma=2.0
./train.py --multi-gpu=3 --batch-size=3 --freeze-backbone \
--no-evaluation --steps=10000 --epochs=20 --snapshot-path xray3-snapshots csv ../\
utils/annotations-with-negatives/train_annotations.csv ../utils/annotations/classes.csv
./train.py --gpu=1 --freeze-backbone --no-evaluation --steps=10000 --epochs=5 \
--snapshot xray3-snapshots/resnet50_csv_20.h5 \
--snapshot-path xray4-snapshots \
csv ../utils/annotations-with-negatives/train_annotations.csv ../utils/annotations/classes.csv
./evaluate.py --save-path results/ --max-detections=7 \
csv ../utils/annotations-with-negatives/test_annotations.csv \
../utils/annotations-with-negatives/classes.csv xray-snapshots/resnet50_csv_11.h5
With the default Focal Loss hyperparamter settings, the mAP is 0.76
A sampling of inital results show below.
Ground-truth bounding-boxes are in green, detections are in blue.