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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: 0.5947786606129398
---
<!-- 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. This is a subset of the artbench-10 dataset, with a train set of 1000 artworks per class and a test set of 100 artworks per class.
It achieves the following results on the evaluation set:
- Loss: 1.1392
- Accuracy: 0.5948
## 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: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5906 | 0.36 | 100 | 1.4709 | 0.4847 |
| 1.3395 | 0.72 | 200 | 1.3208 | 0.5074 |
| 1.1461 | 1.08 | 300 | 1.3363 | 0.5165 |
| 0.9593 | 1.44 | 400 | 1.1790 | 0.5846 |
| 0.8761 | 1.8 | 500 | 1.1252 | 0.5902 |
| 0.5922 | 2.16 | 600 | 1.1392 | 0.5948 |
| 0.4803 | 2.52 | 700 | 1.1560 | 0.5936 |
| 0.4454 | 2.88 | 800 | 1.1545 | 0.6118 |
| 0.2271 | 3.24 | 900 | 1.2284 | 0.6039 |
| 0.207 | 3.6 | 1000 | 1.2625 | 0.5959 |
| 0.1958 | 3.96 | 1100 | 1.2621 | 0.6005 |
### 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):
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()]
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