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vit-artworkclassifier

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vit-artworkclassifier
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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-artworkclassifier
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: artbench10-vit
      split: test
      args: artbench10-vit
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.4887640449438202
---

<!-- 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. -->

# vit-artworkclassifier

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, a subset of the artbench-10 dataset. Train set size 1800, test set size 180, split equally over the 9 classes.
It achieves the following results on the evaluation set:
- Loss: 1.3363
- Accuracy: 0.4888

## 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: 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: 8
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4136        | 1.79  | 100  | 1.5093          | 0.5112   |
| 0.7189        | 3.57  | 200  | 1.3363          | 0.4888   |
| 0.2717        | 5.36  | 300  | 1.4907          | 0.5281   |
| 0.1227        | 7.14  | 400  | 1.4826          | 0.5562   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2

### Code to Run

def vit_classify(image):
    from transformers import ViTFeatureExtractor
    from transformers import ViTForImageClassification
    import torch
    
    vit = ViTForImageClassification.from_pretrained("oschamp/vit-artworkclassifier")
    vit.eval()
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    vit.to(device)
    
    model_name_or_path = 'google/vit-base-patch16-224-in21k'
    feature_extractor = ViTFeatureExtractor.from_pretrained(model_name_or_path)

    #LOAD IMAGE

    encoding = feature_extractor(images=image, return_tensors="pt")
    encoding.keys()

    pixel_values = encoding['pixel_values'].to(device)

    outputs = vit(pixel_values)
    logits = outputs.logits

    prediction = logits.argmax(-1)
    return prediction.item() #vit.config.id2label[prediction.item()]