demo.mp4
models used:
- bbox detector for finding clock face in the image
- classifier for clock orientation estimation
- keypoint detection for center and top
- semantic segmentation for finding clock hands
- KDE for splitting the binary segmentation mask into individual clock hands
| Path | val.1-min_acc | val.10-min_acc | val.60-min_acc |
|---|---|---|---|
| metrics/end_2_end_summary.json | 0.224 | 0.345 | 0.414 |
| Path |
|---|
| Path |
|---|
| Path | eval.iou_score | eval.loss | step | train.iou_score | train.loss |
|---|---|---|---|---|---|
| metrics/segmentation.json | 0.585 | 0.262 | 149 | 0.851 | 0.081 |
flowchart TD
node1["datasets/watch-faces.json.dvc"]
node2["download-images"]
node3["eval-detector"]
node4["eval-end-2-end"]
node5["eval-keypoint"]
node6["eval-segmentation"]
node7["export-detector"]
node8["generate-detection-dataset"]
node9["generate-watch-hands-dataset"]
node10["train-detector"]
node11["train-keypoint"]
node12["train-segmentation"]
node13["update-metrics"]
node1-->node2
node2-->node3
node2-->node4
node2-->node8
node2-->node9
node2-->node11
node2-->node12
node3-->node13
node4-->node13
node5-->node13
node7-->node3
node8-->node7
node8-->node10
node10-->node4
node10-->node5
node10-->node6
node10-->node7
node11-->node4
node11-->node5
node11-->node13
node12-->node4
node12-->node6
node12-->node13
node14["example_data/IMG_1200_720p.mov.dvc"]
node15["render-demo"]
node14-->node15
node16["checkpoints/segmentation.dvc"]
node17["checkpoints/detector.dvc"]
node18["checkpoints/keypoint.dvc"]
Install watch_recognition module, run pip in the main repository dir
pip install watch_recognition/Tested on Python 3.7 and 3.8
Checkout example notebook: notebooks/demo-on-examples.ipynb
TODO
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