---
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: 0.7619047619047619
---

<!-- 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: 1.2147
- Accuracy: 0.7619

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1624        | 0.16  | 100  | 1.0534          | 0.7638   |
| 0.2926        | 0.32  | 200  | 1.3484          | 0.6867   |
| 0.159         | 0.48  | 300  | 0.9484          | 0.7724   |
| 0.2145        | 0.64  | 400  | 1.0014          | 0.7476   |
| 0.1889        | 0.8   | 500  | 1.0321          | 0.7457   |
| 0.3064        | 0.96  | 600  | 1.0795          | 0.7314   |
| 0.2195        | 1.11  | 700  | 0.9886          | 0.7629   |
| 0.2982        | 1.27  | 800  | 1.0292          | 0.7590   |
| 0.2477        | 1.43  | 900  | 1.2391          | 0.7248   |
| 0.3076        | 1.59  | 1000 | 1.1326          | 0.7324   |
| 0.1863        | 1.75  | 1100 | 1.2596          | 0.7048   |
| 0.2577        | 1.91  | 1200 | 1.0649          | 0.7610   |
| 0.1491        | 2.07  | 1300 | 1.1044          | 0.7562   |
| 0.2635        | 2.23  | 1400 | 1.1965          | 0.7448   |
| 0.2597        | 2.39  | 1500 | 1.2241          | 0.7429   |
| 0.2468        | 2.55  | 1600 | 1.1452          | 0.7390   |
| 0.216         | 2.71  | 1700 | 1.2419          | 0.7276   |
| 0.1971        | 2.87  | 1800 | 1.1883          | 0.7362   |
| 0.2071        | 3.03  | 1900 | 1.4659          | 0.6952   |
| 0.1535        | 3.18  | 2000 | 1.0239          | 0.7724   |
| 0.1842        | 3.34  | 2100 | 1.1967          | 0.7390   |
| 0.2087        | 3.5   | 2200 | 1.1403          | 0.7467   |
| 0.1658        | 3.66  | 2300 | 1.2901          | 0.7343   |
| 0.1159        | 3.82  | 2400 | 1.1826          | 0.7438   |
| 0.1498        | 3.98  | 2500 | 1.2627          | 0.7419   |
| 0.135         | 4.14  | 2600 | 1.1383          | 0.76     |
| 0.1492        | 4.3   | 2700 | 1.2310          | 0.7343   |
| 0.0982        | 4.46  | 2800 | 1.4144          | 0.7105   |
| 0.1256        | 4.62  | 2900 | 1.3513          | 0.7171   |
| 0.1544        | 4.78  | 3000 | 1.4280          | 0.7019   |
| 0.0858        | 4.94  | 3100 | 1.2231          | 0.7429   |
| 0.1049        | 5.1   | 3200 | 1.2775          | 0.7352   |
| 0.1361        | 5.25  | 3300 | 1.2840          | 0.7429   |
| 0.1505        | 5.41  | 3400 | 1.3373          | 0.7390   |
| 0.1244        | 5.57  | 3500 | 1.2959          | 0.7438   |
| 0.1114        | 5.73  | 3600 | 1.3181          | 0.7381   |
| 0.0851        | 5.89  | 3700 | 1.3288          | 0.7457   |
| 0.0799        | 6.05  | 3800 | 1.1859          | 0.76     |
| 0.1331        | 6.21  | 3900 | 1.2544          | 0.7371   |
| 0.121         | 6.37  | 4000 | 1.2186          | 0.7533   |
| 0.1276        | 6.53  | 4100 | 1.2964          | 0.7324   |
| 0.1194        | 6.69  | 4200 | 1.1907          | 0.7590   |
| 0.1649        | 6.85  | 4300 | 1.4679          | 0.7105   |
| 0.0558        | 7.01  | 4400 | 1.2028          | 0.7533   |
| 0.0687        | 7.17  | 4500 | 1.3242          | 0.7381   |
| 0.1419        | 7.32  | 4600 | 1.2328          | 0.76     |
| 0.0901        | 7.48  | 4700 | 1.1861          | 0.7676   |
| 0.1181        | 7.64  | 4800 | 1.4031          | 0.7352   |
| 0.1272        | 7.8   | 4900 | 1.3608          | 0.7438   |
| 0.0979        | 7.96  | 5000 | 1.3098          | 0.7495   |
| 0.0805        | 8.12  | 5100 | 1.2445          | 0.7533   |
| 0.0354        | 8.28  | 5200 | 1.2345          | 0.7581   |
| 0.0499        | 8.44  | 5300 | 1.1776          | 0.7571   |
| 0.1046        | 8.6   | 5400 | 1.1939          | 0.76     |
| 0.0912        | 8.76  | 5500 | 1.2373          | 0.7486   |
| 0.0589        | 8.92  | 5600 | 1.2165          | 0.7552   |
| 0.0829        | 9.08  | 5700 | 1.2684          | 0.7505   |
| 0.0897        | 9.24  | 5800 | 1.2467          | 0.7552   |
| 0.1114        | 9.39  | 5900 | 1.2303          | 0.7571   |
| 0.0712        | 9.55  | 6000 | 1.1997          | 0.7638   |
| 0.0621        | 9.71  | 6100 | 1.2094          | 0.7629   |
| 0.037         | 9.87  | 6200 | 1.2147          | 0.7619   |


### Framework versions

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