ultrasound_plane_classification-microsoft-swin-base-patch4-window7-224-undersampling-v1

This model is a fine-tuned version of microsoft/swin-base-patch4-window7-224 on the marconilabmak/hash-ultrasound-image-classification dataset. It achieves the following results on the evaluation set:

• Loss: 0.3529
• Accuracy: 0.9604
• Precision Macro: 0.9544
• Recall Macro: 0.9558
• F1 Macro: 0.9549
• Sensitivity Fetal Brain: 1.0
• Sensitivity Fetal Abdomen: 0.9286
• Sensitivity Fetal Femur: 1.0
• Sensitivity Fetal Thorax: 0.8947
• Specificity Fetal Brain: 1.0
• Specificity Fetal Abdomen: 0.9863
• Specificity Fetal Femur: 0.9870
• Specificity Fetal Thorax: 0.9756

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 Fetal Brain	Sensitivity Fetal Abdomen	Sensitivity Fetal Femur	Sensitivity Fetal Thorax	Specificity Fetal Brain	Specificity Fetal Abdomen	Specificity Fetal Femur	Specificity Fetal Thorax
5.2540	1.0	45	1.1472	0.5862	0.5969	0.5231	0.4872	0.8667	0.8966	0.2766	0.0526	0.9371	0.5379	0.9808	0.9697
1.5447	2.0	90	0.3789	0.8522	0.8447	0.8327	0.8342	0.9667	0.8276	0.9574	0.5789	0.9580	0.9103	0.9679	0.9636
0.6833	3.0	135	0.5568	0.8522	0.9011	0.8087	0.7992	0.9667	1.0	0.9787	0.2895	0.9860	0.8276	0.9808	1.0
0.7808	4.0	180	0.2893	0.9212	0.9459	0.9033	0.9149	0.9667	1.0	0.9362	0.7105	1.0	0.8897	1.0	1.0
0.6671	5.0	225	0.3016	0.9458	0.9525	0.9300	0.9351	0.9833	1.0	1.0	0.7368	1.0	0.9586	0.9679	1.0
0.8642	6.0	270	0.3313	0.9507	0.9586	0.9415	0.9479	1.0	0.9828	0.9149	0.8684	1.0	0.9379	1.0	0.9939
0.0799	7.0	315	0.3368	0.9655	0.9686	0.9576	0.9620	0.9833	1.0	0.9787	0.8684	0.9930	0.9655	1.0	0.9939
0.2535	8.0	360	0.4027	0.9557	0.9664	0.9408	0.9484	1.0	1.0	1.0	0.7632	1.0	0.9379	1.0	1.0
0.1939	9.0	405	0.2511	0.9557	0.9507	0.9489	0.9497	1.0	0.9483	0.9787	0.8684	1.0	0.9724	0.9936	0.9758
0.0000	10.0	450	0.3636	0.9606	0.9627	0.9559	0.9583	0.95	1.0	0.9787	0.8947	1.0	0.9586	1.0	0.9879
0.0078	11.0	495	0.2500	0.9655	0.9623	0.9646	0.9632	0.9667	0.9655	0.9787	0.9474	1.0	0.9793	1.0	0.9758
0.0996	12.0	540	0.2804	0.9803	0.9825	0.9737	0.9772	1.0	1.0	1.0	0.8947	1.0	0.9793	0.9936	1.0
0.0000	13.0	585	0.2539	0.9754	0.9727	0.9706	0.9716	1.0	0.9828	0.9787	0.9211	1.0	0.9862	0.9936	0.9879
0.0007	14.0	630	0.3613	0.9704	0.9732	0.9666	0.9692	0.9667	1.0	0.9787	0.9211	1.0	0.9655	1.0	0.9939
0.1308	15.0	675	0.3994	0.9655	0.9673	0.9586	0.9619	0.9833	0.9828	1.0	0.8684	1.0	0.9655	0.9936	0.9939
0.4742	16.0	720	0.3312	0.9704	0.9743	0.9628	0.9672	1.0	0.9828	1.0	0.8684	0.9930	0.9724	0.9936	1.0
0.0000	17.0	765	0.5241	0.9606	0.9697	0.9536	0.9596	0.9833	1.0	0.9362	0.8947	1.0	0.9448	1.0	1.0

Framework versions

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