ultrasound_plane_quality-swin-base-patch4-window7-224_class_weight

This model is a fine-tuned version of microsoft/swin-base-patch4-window7-224 on the HASH Ultrasound Plane Quality Dataset dataset. It achieves the following results on the evaluation set:

• Loss: 2.8436
• Accuracy: 0.6923
• Precision Macro: 0.5501
• Recall Macro: 0.5104
• F1 Macro: 0.5246
• Sensitivity Good: 0.8291
• Sensitivity Moderate: 0.3421
• Sensitivity Bad: 0.36
• Specificity Good: 0.4921
• Specificity Moderate: 0.8470
• Specificity Bad: 0.9592

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 4
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 16
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 0.1
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision Macro	Recall Macro	F1 Macro	Sensitivity Good	Sensitivity Moderate	Sensitivity Bad	Specificity Good	Specificity Moderate	Specificity Bad
3.9763	1.0	97	1.1896	0.5747	0.3037	0.3445	0.3196	0.7310	0.3026	0.0	0.3016	0.7268	1.0
4.3623	2.0	194	1.1449	0.6900	0.3011	0.3498	0.3167	0.9494	0.0	0.1	0.0952	1.0	0.9413
4.0362	3.0	291	1.1086	0.6923	0.5769	0.4185	0.4198	0.8861	0.2895	0.08	0.3810	0.8470	0.9949
3.7231	4.0	388	1.1964	0.5995	0.3698	0.4232	0.3588	0.7563	0.0132	0.5	0.3889	0.9836	0.7602
3.8047	5.0	485	1.0374	0.5543	0.4471	0.4966	0.4441	0.6076	0.3421	0.54	0.6746	0.8279	0.7628
3.7615	6.0	582	1.1068	0.6244	0.4489	0.4607	0.4519	0.7405	0.3816	0.26	0.5714	0.8005	0.9005
3.5785	7.0	679	1.1072	0.6199	0.4855	0.4875	0.4749	0.7089	0.4737	0.28	0.6111	0.7432	0.9362
2.7876	8.0	776	1.2497	0.5747	0.4527	0.4980	0.4601	0.6329	0.4211	0.44	0.6111	0.8005	0.8316
2.2637	9.0	873	1.6972	0.6290	0.4766	0.5089	0.4865	0.7247	0.3421	0.46	0.5397	0.8579	0.8622
1.8722	10.0	970	1.7193	0.7059	0.5355	0.4450	0.4586	0.8924	0.3026	0.14	0.3492	0.8907	0.9796
1.6991	11.0	1067	1.9214	0.6041	0.4627	0.4915	0.4664	0.6867	0.4079	0.38	0.6190	0.8497	0.8163
2.0717	12.0	1164	2.2192	0.5701	0.4500	0.4882	0.4561	0.6361	0.3684	0.46	0.6111	0.7568	0.8673
1.6707	13.0	1261	3.1380	0.7036	0.4912	0.4252	0.4372	0.9114	0.1842	0.18	0.2857	0.9372	0.9541
0.9623	14.0	1358	3.2447	0.6946	0.5214	0.4867	0.4990	0.8449	0.3553	0.26	0.4365	0.8798	0.9490
1.7668	15.0	1455	3.3081	0.6471	0.4783	0.4870	0.4807	0.7658	0.3553	0.34	0.5873	0.8087	0.9133
1.3514	16.0	1552	4.2638	0.7014	0.4996	0.4422	0.4545	0.9019	0.1447	0.28	0.3016	0.9290	0.9541
0.7454	17.0	1649	4.9444	0.7127	0.5934	0.4683	0.4920	0.8892	0.3158	0.2	0.3492	0.8934	0.9847
0.7730	18.0	1746	4.9574	0.7195	0.5622	0.4727	0.4959	0.9019	0.2763	0.24	0.3730	0.9071	0.9719
0.7580	19.0	1843	5.7157	0.6923	0.5526	0.4899	0.4934	0.8291	0.4605	0.18	0.5159	0.8197	0.9770

Framework versions

• Transformers 5.0.0
• Pytorch 2.10.0+cu128
• Datasets 4.0.0
• Tokenizers 0.22.2