Use Facebook's Detectron2 to train over OpenImages dataset to detect swimming pools in images.
https://detectron2.readthedocs.io/tutorials/datasets.html
cfg.merge_from_file(model_zoo.get_config_file("COCO-Detection/retinanet_R_101_FPN_3x.yaml"))
cfg.DATASETS.TRAIN = ("pool_train",)
cfg.DATASETS.TEST = ()
cfg.DATALOADER.NUM_WORKERS = 2
cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-Detection/retinanet_R_101_FPN_3x.yaml") # Let training initialize from model zoo
cfg.SOLVER.IMS_PER_BATCH = 2
cfg.SOLVER.BASE_LR = 0.00025 # pick a good LR
cfg.SOLVER.MAX_ITER = 2000 # 300 iterations seems good enough for this toy dataset; you may need to train longer for a practical dataset
cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 128 # faster, and good enough for this toy dataset (default: 512)
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 1 # only has one class (pool)
Since we are only detecting images and do not have segmentation masks in the training dataset, I had to use COCO-Detection instead of COCO-InstanceSegmentation
For the detection, I used retinanet_R_101_FPN_3x.yaml (recommendation taken from Bourke's project), and ran it for 2000 iterations. Initially tried with the default 300 iterations, and results were quite bad (loss rate ~0.5). With 2000 iterations, loss rate came down to ~0.2.
Open Images v6 (Category: Swimming Pool) Detectron_Prepare_Own_Dataset notebook shows how I converted from the OI format to detectron's input format.
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.714
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.938
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.785
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.096
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.730
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.686
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.773
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.782
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.350
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.794
Evaluation results for bbox:
| AP | AP50 | AP75 | APs | APm | APl |
|:------:|:------:|:------:|:-----:|:-----:|:------:|
| 71.379 | 93.832 | 78.454 | nan | 9.556 | 72.950 |
