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windowz_dce-022625
mdeputy/windowz_dce-022625
windowz_dce-022625 is a machine learning model from mdeputy. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- Model type
- Unet
- Access
- Public
- Created
- Feb 26, 2025
- Updated
- Feb 26, 2025
- SHA
- 028a85df
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
windowz_dce-022625
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Accuracy: 0.9916
- F1: 0.9914
- Iou: 0.9838
- Per Class Metrics: {0: {'f1': 0.99722, 'iou': 0.99445, 'accuracy': 0.99583}, 1: {'f1': 0.98262, 'iou': 0.96584, 'accuracy': 0.99157}, 2: {'f1': 0.752, 'iou': 0.60257, 'accuracy': 0.9957}}
- Loss: 0.0191
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: 16
- eval_batch_size: 16
- 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: 100
Training results
| Training Loss | Epoch | Step | Class Metrics | Validation Loss | |
|---|---|---|---|---|---|
| 0.4609 | 5.0 | 12815 | 0.9814 | {0: {'f1': 0.99753, 'iou': 0.99506, 'accuracy': 0.9963}, 1: {'f1': 0.97958, 'iou': 0.95998, 'accuracy': 0.99006}, 2: {'f1': 0.62099, 'iou': 0.45032, 'accuracy': 0.99369}} | 0.0445 |
| 0.4462 | 10.0 | 25630 | 0.9830 | {0: {'f1': 0.99759, 'iou': 0.99518, 'accuracy': 0.99639}, 1: {'f1': 0.98171, 'iou': 0.96408, 'accuracy': 0.99106}, 2: {'f1': 0.67311, 'iou': 0.50729, 'accuracy': 0.99467}} | 0.0885 |
| 0.4105 | 15.0 | 38445 | 0.9847 | {0: {'f1': 0.99756, 'iou': 0.99514, 'accuracy': 0.99635}, 1: {'f1': 0.98336, 'iou': 0.96726, 'accuracy': 0.99192}, 2: {'f1': 0.7548, 'iou': 0.60617, 'accuracy': 0.99557}} | 0.0298 |
| 0.4006 | 20.0 | 51260 | 0.9834 | {0: {'f1': 0.99716, 'iou': 0.99433, 'accuracy': 0.99574}, 1: {'f1': 0.98185, 'iou': 0.96435, 'accuracy': 0.99123}, 2: {'f1': 0.75043, 'iou': 0.60056, 'accuracy': 0.99546}} | 0.0201 |
| 0.4016 | 25.0 | 64075 | 0.9838 | {0: {'f1': 0.99722, 'iou': 0.99445, 'accuracy': 0.99583}, 1: {'f1': 0.98262, 'iou': 0.96584, 'accuracy': 0.99157}, 2: {'f1': 0.752, 'iou': 0.60257, 'accuracy': 0.9957}} | 0.0191 |
| 0.3635 | 30.0 | 76890 | 0.9849 | {0: {'f1': 0.99748, 'iou': 0.99496, 'accuracy': 0.99622}, 1: {'f1': 0.98326, 'iou': 0.96706, 'accuracy': 0.99192}, 2: {'f1': 0.78691, 'iou': 0.64868, 'accuracy': 0.99567}} | 0.0195 |
| 0.3754 | 35.0 | 89705 | 0.9822 | {0: {'f1': 0.99678, 'iou': 0.99359, 'accuracy': 0.99518}, 1: {'f1': 0.98001, 'iou': 0.96081, 'accuracy': 0.99042}, 2: {'f1': 0.77117, 'iou': 0.62757, 'accuracy': 0.9952}} | 0.0284 |
| 0.3568 | 40.0 | 102520 | 0.9827 | {0: {'f1': 0.99752, 'iou': 0.99506, 'accuracy': 0.99629}, 1: {'f1': 0.97971, 'iou': 0.96022, 'accuracy': 0.99027}, 2: {'f1': 0.73597, 'iou': 0.58224, 'accuracy': 0.99395}} | 0.0239 |
Framework versions
- Transformers 4.45.0
- Pytorch 2.5.1+cu124
- Datasets 2.21.0
- Tokenizers 0.20.3
- Library
- transformers
- Type
- Unet
- Created
- Feb 26, 2025
- Updated
- Feb 26, 2025