---
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: 0.78
---

<!-- 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: 0.9408
- Accuracy: 0.78

## 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: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1382        | 0.32  | 100  | 1.0002          | 0.7676   |
| 0.782         | 0.64  | 200  | 0.7673          | 0.7676   |
| 0.6289        | 0.96  | 300  | 0.7073          | 0.7867   |
| 0.5028        | 1.27  | 400  | 0.7261          | 0.7686   |
| 0.4746        | 1.59  | 500  | 0.7464          | 0.7619   |
| 0.4298        | 1.91  | 600  | 0.6551          | 0.7990   |
| 0.3488        | 2.23  | 700  | 0.7359          | 0.7733   |
| 0.266         | 2.55  | 800  | 0.8296          | 0.7514   |
| 0.3651        | 2.87  | 900  | 0.8661          | 0.7305   |
| 0.2796        | 3.18  | 1000 | 0.7188          | 0.7867   |
| 0.2703        | 3.5   | 1100 | 0.8422          | 0.7476   |
| 0.2608        | 3.82  | 1200 | 0.8207          | 0.7724   |
| 0.251         | 4.14  | 1300 | 1.0252          | 0.7267   |
| 0.2085        | 4.46  | 1400 | 1.0475          | 0.7171   |
| 0.1715        | 4.78  | 1500 | 0.8852          | 0.7495   |
| 0.2051        | 5.1   | 1600 | 0.8164          | 0.7790   |
| 0.1481        | 5.41  | 1700 | 0.8825          | 0.7629   |
| 0.177         | 5.73  | 1800 | 0.8623          | 0.7867   |
| 0.1607        | 6.05  | 1900 | 0.9487          | 0.7610   |
| 0.1273        | 6.37  | 2000 | 0.8985          | 0.7733   |
| 0.1609        | 6.69  | 2100 | 0.9624          | 0.7505   |
| 0.1583        | 7.01  | 2200 | 0.9015          | 0.7781   |
| 0.1178        | 7.32  | 2300 | 0.9143          | 0.7762   |
| 0.1175        | 7.64  | 2400 | 0.9671          | 0.7590   |
| 0.1257        | 7.96  | 2500 | 0.8925          | 0.7838   |
| 0.0939        | 8.28  | 2600 | 0.9257          | 0.7705   |
| 0.1238        | 8.6   | 2700 | 0.9797          | 0.7648   |
| 0.1219        | 8.92  | 2800 | 0.9399          | 0.7724   |
| 0.0985        | 9.24  | 2900 | 0.9940          | 0.7648   |
| 0.1069        | 9.55  | 3000 | 0.9392          | 0.7743   |
| 0.0589        | 9.87  | 3100 | 0.9408          | 0.78     |


### Framework versions

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