Image Classification
Transformers
PyTorch
TensorBoard
Safetensors
vit
huggingpics
Eval Results (legacy)
Instructions to use ohidaoui/monuments-morocco-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ohidaoui/monuments-morocco-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ohidaoui/monuments-morocco-v1") 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("ohidaoui/monuments-morocco-v1") model = AutoModelForImageClassification.from_pretrained("ohidaoui/monuments-morocco-v1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e28dfb9304d4ea8c7e0b0c5c188495b9f14fe7c550c9e219412f1d919a555acd
- Size of remote file:
- 343 MB
- SHA256:
- f505c120d57cf809f67d247152fed4d4abc61fb9b166ccc1ae0e2657d2a7b7f1
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