---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: project_4_transfer_learning
  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: 0.64375
---

<!-- 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. -->

# project_4_transfer_learning

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: 1.1429
- Accuracy: 0.6438

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| 2.0754        | 1.0   | 10   | 0.125    | 2.0725          |
| 2.0459        | 2.0   | 20   | 0.2625   | 2.0286          |
| 1.968         | 3.0   | 30   | 0.3      | 1.9506          |
| 1.8311        | 4.0   | 40   | 0.4188   | 1.8060          |
| 1.6911        | 5.0   | 50   | 0.4313   | 1.6814          |
| 1.5677        | 6.0   | 60   | 0.4313   | 1.5851          |
| 1.4801        | 7.0   | 70   | 0.4813   | 1.5169          |
| 1.4033        | 8.0   | 80   | 0.4813   | 1.4614          |
| 1.3435        | 9.0   | 90   | 0.475    | 1.4358          |
| 1.3054        | 10.0  | 100  | 0.525    | 1.4292          |
| 1.2532        | 11.0  | 110  | 0.5188   | 1.3942          |
| 1.2178        | 12.0  | 120  | 0.5312   | 1.3684          |
| 1.1857        | 13.0  | 130  | 0.5062   | 1.3599          |
| 1.1558        | 14.0  | 140  | 0.5312   | 1.2992          |
| 1.1118        | 15.0  | 150  | 0.5375   | 1.3217          |
| 1.0967        | 16.0  | 160  | 0.525    | 1.3177          |
| 1.0671        | 17.0  | 170  | 0.5312   | 1.3420          |
| 1.0635        | 18.0  | 180  | 0.5062   | 1.3319          |
| 1.044         | 19.0  | 190  | 0.5813   | 1.2977          |
| 1.037         | 20.0  | 200  | 0.5125   | 1.3127          |
| 1.0743        | 21.0  | 210  | 1.2062   | 0.6062          |
| 1.0454        | 22.0  | 220  | 1.1564   | 0.65            |
| 1.0457        | 23.0  | 230  | 1.1484   | 0.6312          |
| 1.0246        | 24.0  | 240  | 1.1470   | 0.6312          |
| 0.9859        | 25.0  | 250  | 1.1200   | 0.6438          |
| 0.9885        | 26.0  | 260  | 1.1331   | 0.6375          |
| 0.9823        | 27.0  | 270  | 1.1069   | 0.6562          |
| 0.9412        | 28.0  | 280  | 1.1163   | 0.6375          |
| 0.9172        | 29.0  | 290  | 1.1192   | 0.6375          |
| 0.9334        | 30.0  | 300  | 1.1573   | 0.6             |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3