diff --git "a/README.md" "b/README.md" --- "a/README.md" +++ "b/README.md" @@ -23,133 +23,133 @@ IoU metric: bbox ## After training result ``` IoU metric: bbox - Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.049 - Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.083 - Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.059 + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.009 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 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 ] = 0.000 - Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.050 - Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.117 - Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.204 - Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.222 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.004 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.033 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.054 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 ] = 0.025 - Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.238 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.056 ``` ## Config - dataset: NIH - original model: hustvl/yolos-tiny -- lr: 0.0001 -- dropout_rate: 0.15 -- weight_decay: 0.0005 +- lr: 1e-06 +- dropout_rate: 0.1 +- weight_decay: 0.05 - max_epochs: 100 - train samples: 885 ## Logging ### Training process ``` -{'validation_loss': tensor(7.4925, device='cuda:0'), 'validation_loss_ce': tensor(2.5541, device='cuda:0'), 'validation_loss_bbox': tensor(0.5965, device='cuda:0'), 'validation_loss_giou': tensor(0.9779, device='cuda:0'), 'validation_cardinality_error': tensor(99., device='cuda:0')} -{'training_loss': tensor(2.3264, device='cuda:0'), 'train_loss_ce': tensor(0.4880, device='cuda:0'), 'train_loss_bbox': tensor(0.1570, device='cuda:0'), 'train_loss_giou': tensor(0.5268, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.7121, device='cuda:0'), 'validation_loss_ce': tensor(0.4453, device='cuda:0'), 'validation_loss_bbox': tensor(0.2068, device='cuda:0'), 'validation_loss_giou': tensor(0.6163, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} -{'training_loss': tensor(2.8342, device='cuda:0'), 'train_loss_ce': tensor(0.4484, device='cuda:0'), 'train_loss_bbox': tensor(0.2176, device='cuda:0'), 'train_loss_giou': tensor(0.6488, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.2852, device='cuda:0'), 'validation_loss_ce': tensor(0.4441, device='cuda:0'), 'validation_loss_bbox': tensor(0.1582, device='cuda:0'), 'validation_loss_giou': tensor(0.5250, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} -{'training_loss': tensor(2.1692, device='cuda:0'), 'train_loss_ce': tensor(0.5057, device='cuda:0'), 'train_loss_bbox': tensor(0.1269, device='cuda:0'), 'train_loss_giou': tensor(0.5145, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.1756, device='cuda:0'), 'validation_loss_ce': tensor(0.4304, device='cuda:0'), 'validation_loss_bbox': tensor(0.1417, device='cuda:0'), 'validation_loss_giou': tensor(0.5182, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} -{'training_loss': tensor(1.7561, device='cuda:0'), 'train_loss_ce': tensor(0.3625, device='cuda:0'), 'train_loss_bbox': tensor(0.0990, device='cuda:0'), 'train_loss_giou': tensor(0.4493, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.0836, device='cuda:0'), 'validation_loss_ce': tensor(0.4193, device='cuda:0'), 'validation_loss_bbox': tensor(0.1388, device='cuda:0'), 'validation_loss_giou': tensor(0.4853, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} -{'training_loss': tensor(2.2515, device='cuda:0'), 'train_loss_ce': tensor(0.3129, device='cuda:0'), 'train_loss_bbox': tensor(0.1513, device='cuda:0'), 'train_loss_giou': tensor(0.5911, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.2070, device='cuda:0'), 'validation_loss_ce': tensor(0.4197, device='cuda:0'), 'validation_loss_bbox': tensor(0.1543, device='cuda:0'), 'validation_loss_giou': tensor(0.5080, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} -{'training_loss': tensor(1.8786, device='cuda:0'), 'train_loss_ce': tensor(0.3963, device='cuda:0'), 'train_loss_bbox': tensor(0.1198, device='cuda:0'), 'train_loss_giou': tensor(0.4417, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.0831, device='cuda:0'), 'validation_loss_ce': tensor(0.3973, device='cuda:0'), 'validation_loss_bbox': tensor(0.1401, device='cuda:0'), 'validation_loss_giou': tensor(0.4926, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} -{'training_loss': tensor(1.5512, device='cuda:0'), 'train_loss_ce': tensor(0.2639, device='cuda:0'), 'train_loss_bbox': tensor(0.1068, device='cuda:0'), 'train_loss_giou': tensor(0.3767, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.0724, device='cuda:0'), 'validation_loss_ce': tensor(0.3991, device='cuda:0'), 'validation_loss_bbox': 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device='cuda:0'), 'validation_loss_ce': tensor(0.3821, device='cuda:0'), 'validation_loss_bbox': tensor(0.1331, device='cuda:0'), 'validation_loss_giou': tensor(0.4666, device='cuda:0'), 'validation_cardinality_error': tensor(0.8788, device='cuda:0')} -{'training_loss': tensor(2.1461, device='cuda:0'), 'train_loss_ce': tensor(0.4859, device='cuda:0'), 'train_loss_bbox': tensor(0.1561, device='cuda:0'), 'train_loss_giou': tensor(0.4398, device='cuda:0'), 'train_cardinality_error': tensor(0.8000, device='cuda:0'), 'validation_loss': tensor(2.0157, device='cuda:0'), 'validation_loss_ce': tensor(0.3812, device='cuda:0'), 'validation_loss_bbox': tensor(0.1427, device='cuda:0'), 'validation_loss_giou': tensor(0.4606, device='cuda:0'), 'validation_cardinality_error': tensor(0.6364, device='cuda:0')} -{'training_loss': tensor(1.3417, device='cuda:0'), 'train_loss_ce': tensor(0.3160, device='cuda:0'), 'train_loss_bbox': tensor(0.0713, device='cuda:0'), 'train_loss_giou': tensor(0.3345, 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Examples