---
library_name: transformers
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: windowz_dce-022625
  results: []
---

<!-- 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 [](https://huggingface.co/) 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