windowz_test-020625

This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:

• Model Preparation Time: 0.001
• Accuracy: 0.9796
• F1: 0.9749
• Iou: 0.9601
• Contour Dice: 0.9763
• Per Class Metrics: {0: {'f1': 0.99218, 'iou': 0.98448, 'accuracy': 0.98824, 'contour_dice': 0.99218}, 1: {'f1': 0.95952, 'iou': 0.92218, 'accuracy': 0.98031, 'contour_dice': 0.95952}, 2: {'f1': 0.00151, 'iou': 0.00075, 'accuracy': 0.9906, 'contour_dice': 0.00151}}
• Loss: 0.2439

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: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 1000
• num_epochs: 1

Training results

Training Loss	Epoch	Step	Model Preparation Time		Dice	Class Metrics	Validation Loss
1.1036	0.0501	257	0.001	0.5982	0.2778	{0: {'f1': 0.86413, 'iou': 0.76077, 'accuracy': 0.77129, 'contour_dice': 0.86413}, 1: {'f1': 0.21374, 'iou': 0.11966, 'accuracy': 0.76928, 'contour_dice': 0.21374}, 2: {'f1': 0.01912, 'iou': 0.00965, 'accuracy': 0.97722, 'contour_dice': 0.01912}}	1.0658
1.0397	0.1003	514	0.001	0.6281	0.3228	{0: {'f1': 0.87827, 'iou': 0.78297, 'accuracy': 0.79364, 'contour_dice': 0.87827}, 1: {'f1': 0.29681, 'iou': 0.17426, 'accuracy': 0.79658, 'contour_dice': 0.29681}, 2: {'f1': 0.02455, 'iou': 0.01243, 'accuracy': 0.98492, 'contour_dice': 0.02455}}	0.8951
0.9914	0.1504	771	0.001	0.6255	0.3002	{0: {'f1': 0.87683, 'iou': 0.78067, 'accuracy': 0.79052, 'contour_dice': 0.87683}, 1: {'f1': 0.29198, 'iou': 0.17095, 'accuracy': 0.79672, 'contour_dice': 0.29198}, 2: {'f1': 0.00531, 'iou': 0.00266, 'accuracy': 0.98785, 'contour_dice': 0.00531}}	1.0984
0.9099	0.2005	1028	0.001	0.6361	0.3306	{0: {'f1': 0.87929, 'iou': 0.78459, 'accuracy': 0.79547, 'contour_dice': 0.87929}, 1: {'f1': 0.33695, 'iou': 0.20261, 'accuracy': 0.80371, 'contour_dice': 0.33695}, 2: {'f1': 0.00299, 'iou': 0.0015, 'accuracy': 0.9905, 'contour_dice': 0.00299}}	0.8636
0.8914	0.2507	1285	0.001	0.8276	0.7951	{0: {'f1': 0.945, 'iou': 0.89573, 'accuracy': 0.91327, 'contour_dice': 0.945}, 1: {'f1': 0.78751, 'iou': 0.6495, 'accuracy': 0.9121, 'contour_dice': 0.78751}, 2: {'f1': 0.00757, 'iou': 0.0038, 'accuracy': 0.99051, 'contour_dice': 0.00757}}	0.5194
0.8394	0.3008	1542	0.001	0.8440	0.8288	{0: {'f1': 0.94952, 'iou': 0.9039, 'accuracy': 0.92204, 'contour_dice': 0.94952}, 1: {'f1': 0.81787, 'iou': 0.69186, 'accuracy': 0.9188, 'contour_dice': 0.81787}, 2: {'f1': 0.01225, 'iou': 0.00616, 'accuracy': 0.99049, 'contour_dice': 0.01225}}	0.4643
0.8066	0.3510	1799	0.001	0.8633	0.8558	{0: {'f1': 0.95845, 'iou': 0.92021, 'accuracy': 0.93548, 'contour_dice': 0.95845}, 1: {'f1': 0.83803, 'iou': 0.72121, 'accuracy': 0.92909, 'contour_dice': 0.83803}, 2: {'f1': 0.00216, 'iou': 0.00108, 'accuracy': 0.99053, 'contour_dice': 0.00216}}	0.4510
0.7995	0.4011	2056	0.001	0.8625	0.8540	{0: {'f1': 0.95811, 'iou': 0.91959, 'accuracy': 0.9349, 'contour_dice': 0.95811}, 1: {'f1': 0.83706, 'iou': 0.71978, 'accuracy': 0.92889, 'contour_dice': 0.83706}, 2: {'f1': 0.00225, 'iou': 0.00113, 'accuracy': 0.99056, 'contour_dice': 0.00225}}	0.3804
0.7606	0.4512	2313	0.001	0.8932	0.8965	{0: {'f1': 0.96927, 'iou': 0.94038, 'accuracy': 0.95262, 'contour_dice': 0.96927}, 1: {'f1': 0.87794, 'iou': 0.78243, 'accuracy': 0.94526, 'contour_dice': 0.87794}, 2: {'f1': 0.00084, 'iou': 0.00042, 'accuracy': 0.99059, 'contour_dice': 0.00084}}	0.3936
0.7392	0.5014	2570	0.001	0.9602	0.9777	{0: {'f1': 0.99254, 'iou': 0.9852, 'accuracy': 0.98882, 'contour_dice': 0.99254}, 1: {'f1': 0.9586, 'iou': 0.92049, 'accuracy': 0.97966, 'contour_dice': 0.9586}, 2: {'f1': 7e-05, 'iou': 3e-05, 'accuracy': 0.99059, 'contour_dice': 7e-05}}	0.3214
0.7435	0.5515	2827	0.001	0.9601	0.9763	{0: {'f1': 0.99218, 'iou': 0.98448, 'accuracy': 0.98824, 'contour_dice': 0.99218}, 1: {'f1': 0.95952, 'iou': 0.92218, 'accuracy': 0.98031, 'contour_dice': 0.95952}, 2: {'f1': 0.00151, 'iou': 0.00075, 'accuracy': 0.9906, 'contour_dice': 0.00151}}	0.2439
0.7194	0.6016	3084	0.001	0.8241	0.7995	{0: {'f1': 0.94141, 'iou': 0.8893, 'accuracy': 0.90932, 'contour_dice': 0.94141}, 1: {'f1': 0.79144, 'iou': 0.65487, 'accuracy': 0.90763, 'contour_dice': 0.79144}, 2: {'f1': 0.00311, 'iou': 0.00156, 'accuracy': 0.99061, 'contour_dice': 0.00311}}	0.3798
0.701	0.6518	3341	0.001	0.9695	0.9865	{0: {'f1': 0.99549, 'iou': 0.99102, 'accuracy': 0.99324, 'contour_dice': 0.99549}, 1: {'f1': 0.96954, 'iou': 0.94087, 'accuracy': 0.98502, 'contour_dice': 0.96954}, 2: {'f1': 0.00034, 'iou': 0.00017, 'accuracy': 0.99059, 'contour_dice': 0.00034}}	0.2690
0.6962	0.7019	3598	0.001	0.9565	0.9714	{0: {'f1': 0.99065, 'iou': 0.98148, 'accuracy': 0.98591, 'contour_dice': 0.99065}, 1: {'f1': 0.95637, 'iou': 0.91639, 'accuracy': 0.97893, 'contour_dice': 0.95637}, 2: {'f1': 0.00076, 'iou': 0.00038, 'accuracy': 0.9906, 'contour_dice': 0.00076}}	0.2761

Framework versions

• Transformers 4.45.0
• Pytorch 2.5.1+cu124
• Datasets 2.21.0
• Tokenizers 0.20.3