ultrasound_plane_quality-vit-base-patch16-224-in21k

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the HASH Ultrasound Plane Quality Dataset dataset. It achieves the following results on the evaluation set:

Loss: 0.7986
Accuracy: 0.6878
Precision Macro: 0.4300
Recall Macro: 0.4192
F1 Macro: 0.4138
Sensitivity Good: 0.8987
Sensitivity Moderate: 0.0789
Sensitivity Bad: 0.28
Specificity Good: 0.3333
Specificity Moderate: 0.9344
Specificity Bad: 0.9235

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
0.7491	1.0	97	0.7897	0.7149	0.2383	0.3333	0.2779	1.0	0.0	0.0	0.0	1.0	1.0
0.8198	2.0	194	0.7772	0.7149	0.2383	0.3333	0.2779	1.0	0.0	0.0	0.0	1.0	1.0
0.7936	3.0	291	0.7657	0.7149	0.2383	0.3333	0.2779	1.0	0.0	0.0	0.0	1.0	1.0
0.7881	4.0	388	0.7843	0.7149	0.2383	0.3333	0.2779	1.0	0.0	0.0	0.0	1.0	1.0
0.7402	5.0	485	0.7698	0.7195	0.4082	0.3521	0.3177	0.9905	0.0658	0.0	0.0556	0.9863	1.0
0.8278	6.0	582	0.7597	0.7104	0.4491	0.3402	0.2988	0.9873	0.0132	0.02	0.0476	0.9809	0.9974
0.7460	7.0	679	0.7345	0.7149	0.2890	0.3367	0.2877	0.9968	0.0132	0.0	0.0476	0.9836	1.0
0.6520	8.0	776	0.7796	0.7036	0.5336	0.3749	0.3680	0.9462	0.1184	0.06	0.1825	0.9290	0.9949
0.6931	9.0	873	0.7693	0.6923	0.4729	0.3985	0.4009	0.9051	0.2105	0.08	0.2778	0.8962	0.9821
0.4950	10.0	970	0.7777	0.7081	0.5065	0.3770	0.3699	0.9525	0.1184	0.06	0.1905	0.9344	0.9923
0.5758	11.0	1067	0.7998	0.6923	0.4295	0.3685	0.3617	0.9335	0.0921	0.08	0.1905	0.9290	0.9796
0.5309	12.0	1164	0.7492	0.7285	0.5664	0.4749	0.4967	0.9209	0.2237	0.28	0.3333	0.9508	0.9541
0.6394	13.0	1261	0.7851	0.7036	0.4421	0.3628	0.3459	0.9620	0.0263	0.1	0.1032	0.9672	0.9847
0.4421	14.0	1358	0.8546	0.6584	0.4479	0.4365	0.4401	0.8259	0.2237	0.26	0.3968	0.8907	0.9107
0.3832	15.0	1455	0.8272	0.6742	0.4551	0.4283	0.4363	0.8608	0.1842	0.24	0.3651	0.8907	0.9388
0.3875	16.0	1552	0.8478	0.6765	0.4995	0.4103	0.4237	0.8734	0.1974	0.16	0.3095	0.8689	0.9796
0.4443	17.0	1649	0.8860	0.6810	0.4764	0.4401	0.4462	0.8513	0.3289	0.14	0.4048	0.8607	0.9617

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2

Beijuka/ultrasound_plane_quality

ultrasound_plane_quality-vit-base-patch16-224-in21k

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions