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mdeputy/
windowz_test-020525-1

Name: windowz_test-020525-1
Availability: InStock

transformers

safetensors

Unet

generated_from_trainer

endpoints_compatible

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windowz_test-020525-1

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: 0.9855
F1: 0.9845
Iou: 0.9719
Contour Dice: 0.9827
Per Class Metrics: {0: {'f1': 0.99421, 'iou': 0.98849, 'accuracy': 0.99132, 'contour_dice': 0.99421}, 1: {'f1': 0.97069, 'iou': 0.94305, 'accuracy': 0.98574, 'contour_dice': 0.97069}, 2: {'f1': 0.57177, 'iou': 0.40033, 'accuracy': 0.99387, 'contour_dice': 0.57177}}
Loss: 0.1455

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: 2

Training results

Training Loss	Epoch	Step	Model Preparation Time		Dice	Class Metrics	Validation Loss
1.0553	0.1001	513	0.001	0.2042	0.4026	{0: {'f1': 0.00261, 'iou': 0.00131, 'accuracy': 0.25279, 'contour_dice': 0.00261}, 1: {'f1': 0.91168, 'iou': 0.83769, 'accuracy': 0.95417, 'contour_dice': 0.91168}, 2: {'f1': 0.0098, 'iou': 0.00492, 'accuracy': 0.27529, 'contour_dice': 0.0098}}	1.0723
0.9452	0.2002	1026	0.001	0.7779	0.7181	{0: {'f1': 0.92874, 'iou': 0.86697, 'accuracy': 0.88625, 'contour_dice': 0.92874}, 1: {'f1': 0.69461, 'iou': 0.53211, 'accuracy': 0.88238, 'contour_dice': 0.69461}, 2: {'f1': 0.05695, 'iou': 0.02931, 'accuracy': 0.98261, 'contour_dice': 0.05695}}	0.8706
0.8477	0.3002	1539	0.001	0.8084	0.7673	{0: {'f1': 0.93867, 'iou': 0.88442, 'accuracy': 0.90292, 'contour_dice': 0.93867}, 1: {'f1': 0.74567, 'iou': 0.59448, 'accuracy': 0.89835, 'contour_dice': 0.74567}, 2: {'f1': 0.43351, 'iou': 0.27674, 'accuracy': 0.99005, 'contour_dice': 0.43351}}	0.4034
0.8114	0.4003	2052	0.001	0.7795	0.7144	{0: {'f1': 0.92943, 'iou': 0.86817, 'accuracy': 0.88683, 'contour_dice': 0.92943}, 1: {'f1': 0.69334, 'iou': 0.53062, 'accuracy': 0.88266, 'contour_dice': 0.69334}, 2: {'f1': 0.24887, 'iou': 0.14212, 'accuracy': 0.9898, 'contour_dice': 0.24887}}	0.5827
0.7701	0.5004	2565	0.001	0.9032	0.9078	{0: {'f1': 0.97215, 'iou': 0.94582, 'accuracy': 0.95722, 'contour_dice': 0.97215}, 1: {'f1': 0.88981, 'iou': 0.8015, 'accuracy': 0.95011, 'contour_dice': 0.88981}, 2: {'f1': 0.23167, 'iou': 0.13101, 'accuracy': 0.99155, 'contour_dice': 0.23167}}	0.3269
0.7462	0.6005	3078	0.001	0.9018	0.9034	{0: {'f1': 0.97101, 'iou': 0.94366, 'accuracy': 0.9554, 'contour_dice': 0.97101}, 1: {'f1': 0.88956, 'iou': 0.80109, 'accuracy': 0.95028, 'contour_dice': 0.88956}, 2: {'f1': 0.28195, 'iou': 0.16411, 'accuracy': 0.99182, 'contour_dice': 0.28195}}	0.3012
0.7083	0.7005	3591	0.001	0.8870	0.8875	{0: {'f1': 0.96677, 'iou': 0.93567, 'accuracy': 0.94869, 'contour_dice': 0.96677}, 1: {'f1': 0.86981, 'iou': 0.76962, 'accuracy': 0.94193, 'contour_dice': 0.86981}, 2: {'f1': 0.08311, 'iou': 0.04336, 'accuracy': 0.99081, 'contour_dice': 0.08311}}	0.3264
0.6957	0.8006	4104	0.001	0.8885	0.8861	{0: {'f1': 0.96671, 'iou': 0.93556, 'accuracy': 0.94848, 'contour_dice': 0.96671}, 1: {'f1': 0.87151, 'iou': 0.77229, 'accuracy': 0.94332, 'contour_dice': 0.87151}, 2: {'f1': 0.25672, 'iou': 0.14726, 'accuracy': 0.99154, 'contour_dice': 0.25672}}	0.3388
0.6415	0.9007	4617	0.001	0.9228	0.9283	{0: {'f1': 0.97759, 'iou': 0.95616, 'accuracy': 0.96585, 'contour_dice': 0.97759}, 1: {'f1': 0.91553, 'iou': 0.84422, 'accuracy': 0.96085, 'contour_dice': 0.91553}, 2: {'f1': 0.45503, 'iou': 0.29452, 'accuracy': 0.99296, 'contour_dice': 0.45503}}	0.2154
0.66	1.0008	5130	0.001	0.9150	0.9178	{0: {'f1': 0.97496, 'iou': 0.95115, 'accuracy': 0.96161, 'contour_dice': 0.97496}, 1: {'f1': 0.90465, 'iou': 0.82589, 'accuracy': 0.95679, 'contour_dice': 0.90465}, 2: {'f1': 0.509, 'iou': 0.34138, 'accuracy': 0.99326, 'contour_dice': 0.509}}	0.2575
0.627	1.1009	5643	0.001	0.9406	0.9518	{0: {'f1': 0.98469, 'iou': 0.96984, 'accuracy': 0.97676, 'contour_dice': 0.98469}, 1: {'f1': 0.93594, 'iou': 0.87959, 'accuracy': 0.96989, 'contour_dice': 0.93594}, 2: {'f1': 0.32331, 'iou': 0.19282, 'accuracy': 0.99207, 'contour_dice': 0.32331}}	0.2241
0.6033	1.2009	6156	0.001	0.9191	0.9237	{0: {'f1': 0.97668, 'iou': 0.95443, 'accuracy': 0.96429, 'contour_dice': 0.97668}, 1: {'f1': 0.91018, 'iou': 0.83517, 'accuracy': 0.95907, 'contour_dice': 0.91018}, 2: {'f1': 0.42756, 'iou': 0.27191, 'accuracy': 0.99276, 'contour_dice': 0.42756}}	0.2139
0.6268	1.3010	6669	0.001	0.9675	0.9773	{0: {'f1': 0.99254, 'iou': 0.98518, 'accuracy': 0.98877, 'contour_dice': 0.99254}, 1: {'f1': 0.96583, 'iou': 0.93391, 'accuracy': 0.98358, 'contour_dice': 0.96583}, 2: {'f1': 0.59851, 'iou': 0.42705, 'accuracy': 0.9941, 'contour_dice': 0.59851}}	0.1872
0.5698	1.4011	7182	0.001	0.9597	0.9709	{0: {'f1': 0.99051, 'iou': 0.98119, 'accuracy': 0.98568, 'contour_dice': 0.99051}, 1: {'f1': 0.95785, 'iou': 0.91911, 'accuracy': 0.97981, 'contour_dice': 0.95785}, 2: {'f1': 0.46278, 'iou': 0.30105, 'accuracy': 0.99323, 'contour_dice': 0.46278}}	0.1653
0.5933	1.5012	7695	0.001	0.9349	0.9416	{0: {'f1': 0.98167, 'iou': 0.96399, 'accuracy': 0.97209, 'contour_dice': 0.98167}, 1: {'f1': 0.93053, 'iou': 0.87009, 'accuracy': 0.96767, 'contour_dice': 0.93053}, 2: {'f1': 0.45029, 'iou': 0.29056, 'accuracy': 0.99309, 'contour_dice': 0.45029}}	0.1594
0.6071	1.6012	8208	0.001	0.9719	0.9827	{0: {'f1': 0.99421, 'iou': 0.98849, 'accuracy': 0.99132, 'contour_dice': 0.99421}, 1: {'f1': 0.97069, 'iou': 0.94305, 'accuracy': 0.98574, 'contour_dice': 0.97069}, 2: {'f1': 0.57177, 'iou': 0.40033, 'accuracy': 0.99387, 'contour_dice': 0.57177}}	0.1455
0.5867	1.7013	8721	0.001	0.9567	0.9657	{0: {'f1': 0.9889, 'iou': 0.97805, 'accuracy': 0.98323, 'contour_dice': 0.9889}, 1: {'f1': 0.95391, 'iou': 0.91189, 'accuracy': 0.97813, 'contour_dice': 0.95391}, 2: {'f1': 0.59149, 'iou': 0.41994, 'accuracy': 0.99415, 'contour_dice': 0.59149}}	0.1466
0.5937	1.8014	9234	0.001	0.9305	0.9356	{0: {'f1': 0.97999, 'iou': 0.96076, 'accuracy': 0.96946, 'contour_dice': 0.97999}, 1: {'f1': 0.92376, 'iou': 0.85832, 'accuracy': 0.96491, 'contour_dice': 0.92376}, 2: {'f1': 0.55434, 'iou': 0.38345, 'accuracy': 0.99389, 'contour_dice': 0.55434}}	0.2816
0.6021	1.9015	9747	0.001	0.9137	0.9154	{0: {'f1': 0.97439, 'iou': 0.95005, 'accuracy': 0.96068, 'contour_dice': 0.97439}, 1: {'f1': 0.90445, 'iou': 0.82557, 'accuracy': 0.95681, 'contour_dice': 0.90445}, 2: {'f1': 0.46077, 'iou': 0.29935, 'accuracy': 0.99314, 'contour_dice': 0.46077}}	0.1777

Framework versions

Transformers 4.45.0
Pytorch 2.5.1+cu124
Datasets 2.21.0
Tokenizers 0.20.3

mdeputy/windowz_test-020525-1

windowz_test-020525-1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

mdeputy/
windowz_test-020525-1