---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: Action_model
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8664323374340949
---

<!-- 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. -->

# Action_model

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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5153
- Accuracy: 0.8664

## 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: 0.0001
- 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
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2754        | 0.37  | 100  | 1.1163          | 0.7329   |
| 0.9345        | 0.75  | 200  | 0.8296          | 0.7996   |
| 0.8816        | 1.12  | 300  | 0.7156          | 0.8102   |
| 0.7425        | 1.49  | 400  | 0.6529          | 0.8067   |
| 0.6883        | 1.87  | 500  | 0.6079          | 0.8243   |
| 0.5454        | 2.24  | 600  | 0.5605          | 0.8348   |
| 0.5383        | 2.61  | 700  | 0.5571          | 0.8295   |
| 0.5442        | 2.99  | 800  | 0.5864          | 0.8190   |
| 0.3986        | 3.36  | 900  | 0.5632          | 0.8313   |
| 0.3438        | 3.73  | 1000 | 0.5606          | 0.8366   |
| 0.4345        | 4.1   | 1100 | 0.5354          | 0.8366   |
| 0.4523        | 4.48  | 1200 | 0.4988          | 0.8576   |
| 0.3162        | 4.85  | 1300 | 0.5099          | 0.8541   |
| 0.3793        | 5.22  | 1400 | 0.5190          | 0.8436   |
| 0.3228        | 5.6   | 1500 | 0.4589          | 0.8576   |
| 0.1795        | 5.97  | 1600 | 0.5096          | 0.8489   |
| 0.2626        | 6.34  | 1700 | 0.5403          | 0.8489   |
| 0.3041        | 6.72  | 1800 | 0.4908          | 0.8489   |
| 0.1831        | 7.09  | 1900 | 0.5721          | 0.8383   |
| 0.2275        | 7.46  | 2000 | 0.5349          | 0.8313   |
| 0.1762        | 7.84  | 2100 | 0.5204          | 0.8541   |
| 0.2112        | 8.21  | 2200 | 0.5189          | 0.8629   |
| 0.1242        | 8.58  | 2300 | 0.5377          | 0.8471   |
| 0.1207        | 8.96  | 2400 | 0.5325          | 0.8559   |
| 0.1806        | 9.33  | 2500 | 0.5150          | 0.8647   |
| 0.1793        | 9.7   | 2600 | 0.5153          | 0.8664   |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2