transformers tensorboard safetensors vit image-classification generated_from_trainer dataset:imagefolder base_model:google/vit-base-patch16-224-in21k license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: test_trainer
  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: 0.915
---

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# test_trainer

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: 0.8643
- Accuracy: 0.915

## 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: 8
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 125  | 3.6903          | 0.517    |
| No log        | 2.0   | 250  | 2.7990          | 0.553    |
| No log        | 3.0   | 375  | 2.3198          | 0.57     |
| 3.1391        | 4.0   | 500  | 2.0210          | 0.632    |
| 3.1391        | 5.0   | 625  | 1.8298          | 0.638    |
| 3.1391        | 6.0   | 750  | 1.6753          | 0.683    |
| 3.1391        | 7.0   | 875  | 1.5446          | 0.708    |
| 1.7309        | 8.0   | 1000 | 1.4338          | 0.751    |
| 1.7309        | 9.0   | 1125 | 1.3318          | 0.777    |
| 1.7309        | 10.0  | 1250 | 1.2387          | 0.807    |
| 1.7309        | 11.0  | 1375 | 1.1828          | 0.806    |
| 1.2855        | 12.0  | 1500 | 1.1052          | 0.843    |
| 1.2855        | 13.0  | 1625 | 1.0620          | 0.862    |
| 1.2855        | 14.0  | 1750 | 1.0029          | 0.87     |
| 1.2855        | 15.0  | 1875 | 0.9611          | 0.895    |
| 1.0212        | 16.0  | 2000 | 0.9314          | 0.905    |
| 1.0212        | 17.0  | 2125 | 0.9041          | 0.905    |
| 1.0212        | 18.0  | 2250 | 0.8840          | 0.913    |
| 1.0212        | 19.0  | 2375 | 0.8730          | 0.921    |
| 0.8953        | 20.0  | 2500 | 0.8639          | 0.92     |


### Framework versions

- Transformers 4.37.1
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
