MedicalVision/yolos_tiny_50ep

Ejemplos de uso del modelo:

{'size': tensor([512, 512]), 'image_id': tensor([1]), 'class_labels': tensor([4]), 'boxes': tensor([[0.2622, 0.5729, 0.0847, 0.0773]]), 'area': tensor([1717.9431]), 'iscrowd': tensor([0]), 'orig_size': tensor([1024, 1024])}

Para entrenar el modelo:

{'training_loss': tensor(3.9420, device='cuda:0'), 'train_loss_ce': tensor(1.3197, device='cuda:0'), 'train_loss_bbox': tensor(0.2500, device='cuda:0'), 'train_loss_giou': tensor(0.6860, device='cuda:0'), 'train_cardinality_error': tensor(7., device='cuda:0'), 'validation_loss': tensor(3.3165, device='cuda:0'), 'validation_loss_ce': tensor(1.2489, device='cuda:0'), 'validation_loss_bbox': tensor(0.1881, device='cuda:0'), 'validation_loss_giou': tensor(0.5635, device='cuda:0'), 'validation_cardinality_error': tensor(4.0101, device='cuda:0')}

Para la configuración del modelo:

{ 'dataset': 'NIH', 'original_model': 'hustvl/yolos-tiny', 'lr': 1e-05, 'dropout_rate': 0.1, 'weight_decay': 0.05, 'max_epochs': 50, 'train samples': 885 }

Resultados después del entrenamiento:

IoU metric: bbox
Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.008
Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.016
Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.009
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 ] = 0.008
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.060
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.122
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.140
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 ] = 0.000
Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.141