Image Classification
Transformers
PyTorch
TensorBoard
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use Piro17/finetuned-affecthq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Piro17/finetuned-affecthq with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Piro17/finetuned-affecthq") 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("Piro17/finetuned-affecthq") model = AutoModelForImageClassification.from_pretrained("Piro17/finetuned-affecthq") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 77e633dbeaed30618f56152a6a5c01c590728ca90eaeaee7b957802e4db95a89
- Size of remote file:
- 343 MB
- SHA256:
- 4bdfc4d08c3e0402c0e8220f970941a6bf415ef61787ad72f0ee0e02c7070af7
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