transformers tensorboard safetensors vit image-classification generated_from_trainer dataset:imagefolder base_model:google/vit-base-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: image_classification
  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: 0.5625
---

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# image_classification

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2259
- Accuracy: 0.5625

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: polynomial
- num_epochs: 25

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.8751        | 1.0   | 20   | 1.7512          | 0.3      |
| 1.3825        | 2.0   | 40   | 1.4946          | 0.425    |
| 1.1532        | 3.0   | 60   | 1.3387          | 0.45     |
| 0.9865        | 4.0   | 80   | 1.3469          | 0.4562   |
| 0.8767        | 5.0   | 100  | 1.2275          | 0.55     |
| 0.7586        | 6.0   | 120  | 1.2560          | 0.5062   |
| 0.5985        | 7.0   | 140  | 1.2596          | 0.5062   |
| 0.5052        | 8.0   | 160  | 1.3010          | 0.5687   |
| 0.4243        | 9.0   | 180  | 1.2613          | 0.5563   |
| 0.387         | 10.0  | 200  | 1.2750          | 0.5312   |
| 0.3529        | 11.0  | 220  | 1.3103          | 0.55     |
| 0.218         | 12.0  | 240  | 1.1832          | 0.55     |
| 0.2428        | 13.0  | 260  | 1.2527          | 0.5563   |
| 0.2399        | 14.0  | 280  | 1.4836          | 0.5375   |
| 0.218         | 15.0  | 300  | 1.4056          | 0.4875   |
| 0.1784        | 16.0  | 320  | 1.3879          | 0.5563   |
| 0.2021        | 17.0  | 340  | 1.4346          | 0.5375   |
| 0.1342        | 18.0  | 360  | 1.4666          | 0.4813   |
| 0.1499        | 19.0  | 380  | 1.4104          | 0.5687   |
| 0.1032        | 20.0  | 400  | 1.5402          | 0.525    |
| 0.1214        | 21.0  | 420  | 1.4114          | 0.55     |
| 0.153         | 22.0  | 440  | 1.5887          | 0.525    |
| 0.1276        | 23.0  | 460  | 1.4588          | 0.5188   |
| 0.1114        | 24.0  | 480  | 1.4866          | 0.5312   |
| 0.1305        | 25.0  | 500  | 1.4203          | 0.5687   |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
