| | --- |
| | 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()] |
| | |
