You can deploy this model and start earning money today!

mdeputy/
windowz_ln_segment_051525

Name: windowz_ln_segment_051525
Availability: InStock

transformers

safetensors

Unet

generated_from_trainer

endpoints_compatible

Views

No views yet

windowz_ln_segment_051525

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

Accuracy: 0.9734
F1: 0.9770
Iou: 0.9606
Per Class Metrics: {0: {'f1': 0.99208, 'iou': 0.98429, 'accuracy': 0.98821}, 1: {'f1': 0.95742, 'iou': 0.91831, 'accuracy': 0.97945}, 2: {'f1': 0.27975, 'iou': 0.16263, 'accuracy': 0.97907}}
Loss: 0.5105

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: 24
eval_batch_size: 24
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		Class Metrics	Validation Loss
No log	0.0503	86	0.1155	{0: {'f1': 1e-05, 'iou': 0.0, 'accuracy': 0.25189}, 1: {'f1': 0.64518, 'iou': 0.47621, 'accuracy': 0.86451}, 2: {'f1': 0.00676, 'iou': 0.00339, 'accuracy': 0.13587}}	1.0017
1.2378	0.1006	172	0.0635	{0: {'f1': 2e-05, 'iou': 1e-05, 'accuracy': 0.25181}, 1: {'f1': 0.41464, 'iou': 0.26154, 'accuracy': 0.81337}, 2: {'f1': 0.01135, 'iou': 0.00571, 'accuracy': 0.07762}}	0.9440
1.2207	0.1510	258	0.0554	{0: {'f1': 0.0, 'iou': 0.0, 'accuracy': 0.25189}, 1: {'f1': 0.37135, 'iou': 0.22801, 'accuracy': 0.80693}, 2: {'f1': 0.01383, 'iou': 0.00696, 'accuracy': 0.0683}}	0.9237
1.1925	0.2013	344	0.0499	{0: {'f1': 0.0, 'iou': 0.0, 'accuracy': 0.2519}, 1: {'f1': 0.34062, 'iou': 0.20527, 'accuracy': 0.80395}, 2: {'f1': 0.0169, 'iou': 0.00852, 'accuracy': 0.06157}}	0.9326
1.1606	0.2516	430	0.0647	{0: {'f1': 0.0, 'iou': 0.0, 'accuracy': 0.2519}, 1: {'f1': 0.42092, 'iou': 0.26656, 'accuracy': 0.81803}, 2: {'f1': 0.01824, 'iou': 0.0092, 'accuracy': 0.07944}}	0.9245
1.1299	0.3019	516	0.1179	{0: {'f1': 0.00055, 'iou': 0.00027, 'accuracy': 0.2521}, 1: {'f1': 0.65335, 'iou': 0.48517, 'accuracy': 0.87296}, 2: {'f1': 0.01684, 'iou': 0.00849, 'accuracy': 0.12971}}	0.8751
1.0998	0.3523	602	0.1795	{0: {'f1': 0.09312, 'iou': 0.04883, 'accuracy': 0.28836}, 1: {'f1': 0.74168, 'iou': 0.58942, 'accuracy': 0.89807}, 2: {'f1': 0.01625, 'iou': 0.00819, 'accuracy': 0.19265}}	0.8243
1.0998	0.4026	688	0.8495	{0: {'f1': 0.98464, 'iou': 0.96975, 'accuracy': 0.97734}, 1: {'f1': 0.67506, 'iou': 0.5095, 'accuracy': 0.879}, 2: {'f1': 0.10072, 'iou': 0.05303, 'accuracy': 0.86349}}	0.8043
1.0696	0.4529	774	0.874	{0: {'f1': 0.98422, 'iou': 0.96893, 'accuracy': 0.97671}, 1: {'f1': 0.75979, 'iou': 0.61262, 'accuracy': 0.90403}, 2: {'f1': 0.11786, 'iou': 0.06262, 'accuracy': 0.88981}}	0.7506
1.0419	0.5032	860	0.9391	{0: {'f1': 0.98895, 'iou': 0.97813, 'accuracy': 0.98362}, 1: {'f1': 0.9206, 'iou': 0.85288, 'accuracy': 0.96291}, 2: {'f1': 0.10984, 'iou': 0.05811, 'accuracy': 0.9543}}	0.6618
1.0149	0.5535	946	0.7046	{0: {'f1': 0.91333, 'iou': 0.84049, 'accuracy': 0.85914}, 1: {'f1': 0.47479, 'iou': 0.3113, 'accuracy': 0.83125}, 2: {'f1': 0.06969, 'iou': 0.0361, 'accuracy': 0.95034}}	0.7884
1.0024	0.6039	1032	0.8763	{0: {'f1': 0.97412, 'iou': 0.94954, 'accuracy': 0.96061}, 1: {'f1': 0.81027, 'iou': 0.68105, 'accuracy': 0.92142}, 2: {'f1': 0.15071, 'iou': 0.0815, 'accuracy': 0.94565}}	0.5940
0.9739	0.6542	1118	0.2909	{0: {'f1': 0.24044, 'iou': 0.13665, 'accuracy': 0.34611}, 1: {'f1': 0.87511, 'iou': 0.77796, 'accuracy': 0.94508}, 2: {'f1': 0.01577, 'iou': 0.00795, 'accuracy': 0.31169}}	0.9626
0.9626	0.7045	1204	0.9520	{0: {'f1': 0.99067, 'iou': 0.98152, 'accuracy': 0.98615}, 1: {'f1': 0.94406, 'iou': 0.89405, 'accuracy': 0.97333}, 2: {'f1': 0.17721, 'iou': 0.09722, 'accuracy': 0.96864}}	0.5658
0.9626	0.7548	1290	0.9183	{0: {'f1': 0.98633, 'iou': 0.97303, 'accuracy': 0.97941}, 1: {'f1': 0.87697, 'iou': 0.78089, 'accuracy': 0.94587}, 2: {'f1': 0.18891, 'iou': 0.1043, 'accuracy': 0.95606}}	0.6192
0.9515	0.8051	1376	0.3322	{0: {'f1': 0.25117, 'iou': 0.14362, 'accuracy': 0.35705}, 1: {'f1': 0.96192, 'iou': 0.92663, 'accuracy': 0.98155}, 2: {'f1': 0.00532, 'iou': 0.00267, 'accuracy': 0.34648}}	0.9133
0.9395	0.8555	1462	0.9488	{0: {'f1': 0.99219, 'iou': 0.98451, 'accuracy': 0.98835}, 1: {'f1': 0.93039, 'iou': 0.86984, 'accuracy': 0.96766}, 2: {'f1': 0.25682, 'iou': 0.14733, 'accuracy': 0.96767}}	0.5446
0.9288	0.9058	1548	0.9606	{0: {'f1': 0.99208, 'iou': 0.98429, 'accuracy': 0.98821}, 1: {'f1': 0.95742, 'iou': 0.91831, 'accuracy': 0.97945}, 2: {'f1': 0.27975, 'iou': 0.16263, 'accuracy': 0.97907}}	0.5105
0.9341	0.9561	1634	0.9536	{0: {'f1': 0.98841, 'iou': 0.97708, 'accuracy': 0.98281}, 1: {'f1': 0.95511, 'iou': 0.91408, 'accuracy': 0.97815}, 2: {'f1': 0.18082, 'iou': 0.0994, 'accuracy': 0.97498}}	0.5982

Framework versions

Transformers 4.45.0
Pytorch 2.5.1+cu124
Datasets 3.6.0
Tokenizers 0.20.3

mdeputy/windowz_ln_segment_051525

windowz_ln_segment_051525

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

mdeputy/
windowz_ln_segment_051525