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---
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
|