---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: finetuned-electrical-images
  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. -->

# finetuned-electrical-images

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3787
- Accuracy: 0.9076

## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.7236        | 0.4651 | 100  | 0.6396          | 0.8102   |
| 0.7243        | 0.9302 | 200  | 0.5124          | 0.8333   |
| 0.4288        | 1.3953 | 300  | 0.4514          | 0.8630   |
| 0.5744        | 1.8605 | 400  | 0.6154          | 0.8102   |
| 0.4077        | 2.3256 | 500  | 0.4612          | 0.8614   |
| 0.496         | 2.7907 | 600  | 0.4359          | 0.8729   |
| 0.3446        | 3.2558 | 700  | 0.4276          | 0.8696   |
| 0.3347        | 3.7209 | 800  | 0.4259          | 0.8795   |
| 0.3868        | 4.1860 | 900  | 0.4642          | 0.8548   |
| 0.36          | 4.6512 | 1000 | 0.4242          | 0.8696   |
| 0.295         | 5.1163 | 1100 | 0.4204          | 0.8812   |
| 0.2342        | 5.5814 | 1200 | 0.3933          | 0.8911   |
| 0.1629        | 6.0465 | 1300 | 0.3634          | 0.8977   |
| 0.2041        | 6.5116 | 1400 | 0.4007          | 0.8911   |
| 0.1668        | 6.9767 | 1500 | 0.3843          | 0.8927   |
| 0.0976        | 7.4419 | 1600 | 0.4062          | 0.8927   |
| 0.1275        | 7.9070 | 1700 | 0.3861          | 0.8894   |
| 0.1063        | 8.3721 | 1800 | 0.4011          | 0.8911   |
| 0.1658        | 8.8372 | 1900 | 0.3840          | 0.9043   |
| 0.1           | 9.3023 | 2000 | 0.3873          | 0.9010   |
| 0.1045        | 9.7674 | 2100 | 0.3787          | 0.9076   |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1