UAVS model trained on all verified object classes, collapsed to only one class: - object.
Classes collapsed to object class
Input images: 2935; transformed images: 78491
Input labels: {'Whale': 1, 'Bird': 6628, 'Egregia': 302, 'Kelp': 25263, 'Jelly': 372, 'Boat': 10, 'Mooring_Buoy': 3, 'Batray': 354, 'Otter': 12, 'Velella_velella_raft': 74, 'Cement_Ship': 23, 'Shark': 28, 'Surfboard': 5, 'Pinniped': 72, 'Kayak': 5, 'Velella_velella': 29, 'Fish': 13, 'Mola': 4, 'Person': 10};
Acts as object detector to feed detection stage of a pipeline
rf-detr Large trained 20 epochs on GB-10 batch 16 grad-accum-steps 1
resolution 728 pixels
Validation results: using checkpoint_best_total.pth
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.595
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.793
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.633
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.459
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.539
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.635
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.341
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.672
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.771
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.599
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.700
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.820
Training time 2 days, 16:54:24
Results saved to output/results.json
metrics_plotTest results: using checkpoint_best_total.pth
'map@50:95': 0.594791695781542,
'map@50': 0.7989944041962765,
'precision': 0.8687089715536105,
'recall': 0.7000000000000001
