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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- imagefolder |
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metrics: |
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- accuracy |
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model-index: |
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- name: vit-artworkclassifier |
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results: |
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- task: |
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name: Image Classification |
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type: image-classification |
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dataset: |
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name: imagefolder |
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type: imagefolder |
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config: artbench10-vit |
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split: test |
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args: artbench10-vit |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.4887640449438202 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# vit-artworkclassifier |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.3363 |
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- Accuracy: 0.4888 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 32 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 8 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 1.4136 | 1.79 | 100 | 1.5093 | 0.5112 | |
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| 0.7189 | 3.57 | 200 | 1.3363 | 0.4888 | |
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| 0.2717 | 5.36 | 300 | 1.4907 | 0.5281 | |
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| 0.1227 | 7.14 | 400 | 1.4826 | 0.5562 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 1.13.1+cu117 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.2 |
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### Code to Run |
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def vit_classify(image): |
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from transformers import ViTFeatureExtractor |
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from transformers import ViTForImageClassification |
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import torch |
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vit = ViTForImageClassification.from_pretrained("oschamp/vit-artworkclassifier") |
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vit.eval() |
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') |
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vit.to(device) |
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model_name_or_path = 'google/vit-base-patch16-224-in21k' |
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feature_extractor = ViTFeatureExtractor.from_pretrained(model_name_or_path) |
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#LOAD IMAGE |
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encoding = feature_extractor(images=image, return_tensors="pt") |
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encoding.keys() |
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pixel_values = encoding['pixel_values'].to(device) |
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outputs = vit(pixel_values) |
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logits = outputs.logits |
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prediction = logits.argmax(-1) |
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return prediction.item() #vit.config.id2label[prediction.item()] |
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