Image Classification
Transformers
PyTorch
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use oschamp/vit-artworkclassifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use oschamp/vit-artworkclassifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="oschamp/vit-artworkclassifier") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("oschamp/vit-artworkclassifier") model = AutoModelForImageClassification.from_pretrained("oschamp/vit-artworkclassifier") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -85,7 +85,7 @@ The following hyperparameters were used during training:
|
|
| 85 |
- Tokenizers 0.13.2
|
| 86 |
|
| 87 |
### Code to Run
|
| 88 |
-
|
| 89 |
def vit_classify(image):
|
| 90 |
vit = ViTForImageClassification.from_pretrained("oschamp/vit-artworkclassifier")
|
| 91 |
vit.eval()
|
|
@@ -107,4 +107,4 @@ def vit_classify(image):
|
|
| 107 |
|
| 108 |
prediction = logits.argmax(-1)
|
| 109 |
return prediction.item() #vit.config.id2label[prediction.item()]
|
| 110 |
-
|
|
|
|
| 85 |
- Tokenizers 0.13.2
|
| 86 |
|
| 87 |
### Code to Run
|
| 88 |
+
```
|
| 89 |
def vit_classify(image):
|
| 90 |
vit = ViTForImageClassification.from_pretrained("oschamp/vit-artworkclassifier")
|
| 91 |
vit.eval()
|
|
|
|
| 107 |
|
| 108 |
prediction = logits.argmax(-1)
|
| 109 |
return prediction.item() #vit.config.id2label[prediction.item()]
|
| 110 |
+
```
|