transformers safetensors vit image-classification generated_from_trainer base_model:google/vit-base-patch16-224 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: ProteinClassificationModel
  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. -->

# ProteinClassificationModel

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5405
- Accuracy: 0.9

## 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: 2e-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: linear
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7133        | 1.0   | 113  | 0.3957          | 0.84     |
| 0.3139        | 2.0   | 226  | 0.3768          | 0.845    |
| 0.2595        | 3.0   | 339  | 0.3597          | 0.89     |
| 0.2337        | 4.0   | 452  | 0.3059          | 0.9      |
| 0.2152        | 5.0   | 565  | 0.3772          | 0.88     |
| 0.147         | 6.0   | 678  | 0.4119          | 0.885    |
| 0.1592        | 7.0   | 791  | 0.3554          | 0.895    |
| 0.1162        | 8.0   | 904  | 0.3889          | 0.88     |
| 0.1105        | 9.0   | 1017 | 0.5130          | 0.855    |
| 0.0968        | 10.0  | 1130 | 0.4606          | 0.87     |
| 0.1197        | 11.0  | 1243 | 0.4978          | 0.88     |
| 0.1103        | 12.0  | 1356 | 0.4679          | 0.885    |
| 0.089         | 13.0  | 1469 | 0.4860          | 0.885    |
| 0.0696        | 14.0  | 1582 | 0.5217          | 0.87     |
| 0.0667        | 15.0  | 1695 | 0.5027          | 0.885    |
| 0.0647        | 16.0  | 1808 | 0.4870          | 0.89     |
| 0.0577        | 17.0  | 1921 | 0.4739          | 0.875    |
| 0.0475        | 18.0  | 2034 | 0.4714          | 0.885    |
| 0.0479        | 19.0  | 2147 | 0.4766          | 0.875    |
| 0.0563        | 20.0  | 2260 | 0.5175          | 0.89     |
| 0.0356        | 21.0  | 2373 | 0.5672          | 0.9      |
| 0.052         | 22.0  | 2486 | 0.4877          | 0.895    |
| 0.0385        | 23.0  | 2599 | 0.5096          | 0.895    |
| 0.0321        | 24.0  | 2712 | 0.4934          | 0.905    |
| 0.0295        | 25.0  | 2825 | 0.4869          | 0.9      |
| 0.0347        | 26.0  | 2938 | 0.5327          | 0.905    |
| 0.025         | 27.0  | 3051 | 0.5653          | 0.9      |
| 0.0382        | 28.0  | 3164 | 0.5227          | 0.9      |
| 0.0167        | 29.0  | 3277 | 0.5447          | 0.9      |
| 0.0254        | 30.0  | 3390 | 0.5405          | 0.9      |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
