transformers safetensors vit image-classification generated_from_trainer base_model:google/vit-base-patch16-224-in21k license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: protein-classification
  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. -->

# protein-classification

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: 0.3403
- Accuracy: 0.905

## 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: 10
- eval_batch_size: 4
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 180  | 0.4654          | 0.855    |
| No log        | 2.0   | 360  | 0.4703          | 0.835    |
| 0.521         | 3.0   | 540  | 0.3485          | 0.88     |
| 0.521         | 4.0   | 720  | 0.3523          | 0.865    |
| 0.521         | 5.0   | 900  | 0.3411          | 0.89     |
| 0.3122        | 6.0   | 1080 | 0.2946          | 0.91     |
| 0.3122        | 7.0   | 1260 | 0.3441          | 0.865    |
| 0.3122        | 8.0   | 1440 | 0.2862          | 0.915    |
| 0.2774        | 9.0   | 1620 | 0.3051          | 0.9      |
| 0.2774        | 10.0  | 1800 | 0.3358          | 0.895    |
| 0.2774        | 11.0  | 1980 | 0.3127          | 0.915    |
| 0.2688        | 12.0  | 2160 | 0.3054          | 0.895    |
| 0.2688        | 13.0  | 2340 | 0.3246          | 0.89     |
| 0.2316        | 14.0  | 2520 | 0.3374          | 0.91     |
| 0.2316        | 15.0  | 2700 | 0.4155          | 0.875    |
| 0.2316        | 16.0  | 2880 | 0.3715          | 0.885    |
| 0.2066        | 17.0  | 3060 | 0.3345          | 0.91     |
| 0.2066        | 18.0  | 3240 | 0.3400          | 0.9      |
| 0.2066        | 19.0  | 3420 | 0.3190          | 0.9      |
| 0.1893        | 20.0  | 3600 | 0.3084          | 0.915    |
| 0.1893        | 21.0  | 3780 | 0.3875          | 0.89     |
| 0.1893        | 22.0  | 3960 | 0.3599          | 0.89     |
| 0.1835        | 23.0  | 4140 | 0.3633          | 0.905    |
| 0.1835        | 24.0  | 4320 | 0.2984          | 0.905    |
| 0.1665        | 25.0  | 4500 | 0.3005          | 0.91     |
| 0.1665        | 26.0  | 4680 | 0.3371          | 0.915    |
| 0.1665        | 27.0  | 4860 | 0.3860          | 0.89     |
| 0.1627        | 28.0  | 5040 | 0.3110          | 0.915    |
| 0.1627        | 29.0  | 5220 | 0.3365          | 0.905    |
| 0.1627        | 30.0  | 5400 | 0.3403          | 0.905    |


### Framework versions

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