---
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: 0.7676190476190476
---

<!-- 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: 0.7365
- Accuracy: 0.7676

## 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: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3489        | 0.16  | 100  | 1.2612          | 0.7      |
| 1.0112        | 0.32  | 200  | 0.9050          | 0.7590   |
| 0.7962        | 0.48  | 300  | 0.8522          | 0.7505   |
| 0.6383        | 0.64  | 400  | 0.8676          | 0.7219   |
| 0.6485        | 0.8   | 500  | 0.8052          | 0.7324   |
| 0.5452        | 0.96  | 600  | 0.7120          | 0.7848   |
| 0.4882        | 1.11  | 700  | 0.7478          | 0.7714   |
| 0.3409        | 1.27  | 800  | 0.7311          | 0.7743   |
| 0.4105        | 1.43  | 900  | 0.7353          | 0.7810   |
| 0.4011        | 1.59  | 1000 | 0.8154          | 0.7457   |
| 0.3493        | 1.75  | 1100 | 0.7398          | 0.7752   |
| 0.3389        | 1.91  | 1200 | 0.7365          | 0.7676   |


### Framework versions

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