---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- 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 the Electrical_components(VIT) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3726
- Accuracy: 0.8861

## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.7116        | 0.4651 | 100  | 0.6399          | 0.7921   |
| 0.6953        | 0.9302 | 200  | 0.5589          | 0.8086   |
| 0.4078        | 1.3953 | 300  | 0.4946          | 0.8399   |
| 0.5852        | 1.8605 | 400  | 0.4872          | 0.8399   |
| 0.4993        | 2.3256 | 500  | 0.4687          | 0.8597   |
| 0.4479        | 2.7907 | 600  | 0.3986          | 0.8845   |
| 0.4101        | 3.2558 | 700  | 0.4385          | 0.8729   |
| 0.283         | 3.7209 | 800  | 0.4413          | 0.8762   |
| 0.3959        | 4.1860 | 900  | 0.4121          | 0.8729   |
| 0.318         | 4.6512 | 1000 | 0.4397          | 0.8696   |
| 0.2401        | 5.1163 | 1100 | 0.4887          | 0.8680   |
| 0.1273        | 5.5814 | 1200 | 0.4224          | 0.8663   |
| 0.1101        | 6.0465 | 1300 | 0.4378          | 0.8779   |
| 0.1773        | 6.5116 | 1400 | 0.3730          | 0.8845   |
| 0.2248        | 6.9767 | 1500 | 0.3726          | 0.8861   |
| 0.0987        | 7.4419 | 1600 | 0.4398          | 0.8845   |
| 0.16          | 7.9070 | 1700 | 0.4171          | 0.8828   |
| 0.1224        | 8.3721 | 1800 | 0.4336          | 0.8878   |
| 0.2111        | 8.8372 | 1900 | 0.3948          | 0.8944   |
| 0.112         | 9.3023 | 2000 | 0.4004          | 0.8944   |
| 0.0962        | 9.7674 | 2100 | 0.4092          | 0.8927   |


### Framework versions

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