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:
- 74c4035fa2260a14420916ead9511430d7a026310000d14c32598073e9b362d5
- Size of remote file:
- 145 kB
- SHA256:
- 1e6b3db71c38e200d1f541c7ea1ab2baebf4248f4ec13d94041ff07f1e41e259
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