---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: Action_agent
  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: 0.8019047619047619
---

<!-- 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_agent

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: 0.6758
- Accuracy: 0.8019

## 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: 1e-05
- 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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1987        | 0.32  | 100  | 2.1640          | 0.3914   |
| 1.9807        | 0.64  | 200  | 1.9169          | 0.6143   |
| 1.6738        | 0.96  | 300  | 1.6148          | 0.72     |
| 1.4828        | 1.27  | 400  | 1.3861          | 0.7705   |
| 1.2768        | 1.59  | 500  | 1.2412          | 0.7590   |
| 1.1759        | 1.91  | 600  | 1.1169          | 0.7914   |
| 1.0314        | 2.23  | 700  | 1.0599          | 0.7762   |
| 0.9702        | 2.55  | 800  | 0.9640          | 0.8105   |
| 0.9559        | 2.87  | 900  | 0.9138          | 0.8076   |
| 0.858         | 3.18  | 1000 | 0.8605          | 0.8248   |
| 0.7858        | 3.5   | 1100 | 0.8164          | 0.8371   |
| 0.7898        | 3.82  | 1200 | 0.7917          | 0.8333   |
| 0.6909        | 4.14  | 1300 | 0.7995          | 0.8038   |
| 0.6619        | 4.46  | 1400 | 0.8194          | 0.7829   |
| 0.6457        | 4.78  | 1500 | 0.7536          | 0.8086   |
| 0.6155        | 5.1   | 1600 | 0.7212          | 0.8257   |
| 0.5511        | 5.41  | 1700 | 0.7274          | 0.8095   |
| 0.5486        | 5.73  | 1800 | 0.7048          | 0.8286   |
| 0.5679        | 6.05  | 1900 | 0.7124          | 0.8181   |
| 0.4914        | 6.37  | 2000 | 0.7277          | 0.8010   |
| 0.525         | 6.69  | 2100 | 0.6971          | 0.8124   |
| 0.5081        | 7.01  | 2200 | 0.6869          | 0.8162   |
| 0.5072        | 7.32  | 2300 | 0.6837          | 0.8076   |
| 0.4702        | 7.64  | 2400 | 0.6736          | 0.8152   |
| 0.4303        | 7.96  | 2500 | 0.6693          | 0.8105   |
| 0.3916        | 8.28  | 2600 | 0.6487          | 0.8238   |
| 0.4002        | 8.6   | 2700 | 0.6661          | 0.8162   |
| 0.3965        | 8.92  | 2800 | 0.6611          | 0.8143   |
| 0.3946        | 9.24  | 2900 | 0.6523          | 0.8143   |
| 0.3794        | 9.55  | 3000 | 0.6616          | 0.8048   |
| 0.3257        | 9.87  | 3100 | 0.6717          | 0.8029   |
| 0.4175        | 10.19 | 3200 | 0.6530          | 0.8057   |
| 0.3559        | 10.51 | 3300 | 0.6883          | 0.7886   |
| 0.3824        | 10.83 | 3400 | 0.6611          | 0.8      |
| 0.3589        | 11.15 | 3500 | 0.6659          | 0.8019   |
| 0.3299        | 11.46 | 3600 | 0.6819          | 0.7962   |
| 0.3736        | 11.78 | 3700 | 0.6405          | 0.8114   |
| 0.3576        | 12.1  | 3800 | 0.6725          | 0.7962   |
| 0.3454        | 12.42 | 3900 | 0.7025          | 0.7943   |
| 0.3049        | 12.74 | 4000 | 0.6439          | 0.8133   |
| 0.3363        | 13.06 | 4100 | 0.6352          | 0.8143   |
| 0.3273        | 13.38 | 4200 | 0.6795          | 0.7886   |
| 0.283         | 13.69 | 4300 | 0.6705          | 0.8      |
| 0.2607        | 14.01 | 4400 | 0.6732          | 0.7914   |
| 0.3174        | 14.33 | 4500 | 0.6691          | 0.8048   |
| 0.3189        | 14.65 | 4600 | 0.6602          | 0.8038   |
| 0.2862        | 14.97 | 4700 | 0.6801          | 0.7933   |
| 0.2895        | 15.29 | 4800 | 0.6579          | 0.8038   |
| 0.263         | 15.61 | 4900 | 0.6688          | 0.8      |
| 0.3214        | 15.92 | 5000 | 0.6547          | 0.8057   |
| 0.2867        | 16.24 | 5100 | 0.6775          | 0.7924   |
| 0.2242        | 16.56 | 5200 | 0.6378          | 0.8086   |
| 0.2839        | 16.88 | 5300 | 0.6761          | 0.7990   |
| 0.2424        | 17.2  | 5400 | 0.6386          | 0.8124   |
| 0.2666        | 17.52 | 5500 | 0.6493          | 0.8133   |
| 0.2259        | 17.83 | 5600 | 0.6514          | 0.8048   |
| 0.2533        | 18.15 | 5700 | 0.6676          | 0.8      |
| 0.2697        | 18.47 | 5800 | 0.6705          | 0.8010   |
| 0.2558        | 18.79 | 5900 | 0.6750          | 0.8076   |
| 0.2469        | 19.11 | 6000 | 0.6751          | 0.7990   |
| 0.284         | 19.43 | 6100 | 0.6738          | 0.7981   |
| 0.2534        | 19.75 | 6200 | 0.6758          | 0.8019   |


### Framework versions

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