Image Classification
Transformers
TensorBoard
Safetensors
PyTorch
vit
huggingpics
Eval Results (legacy)
Instructions to use holyspark/Moto_Classification_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use holyspark/Moto_Classification_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="holyspark/Moto_Classification_v2") 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("holyspark/Moto_Classification_v2") model = AutoModelForImageClassification.from_pretrained("holyspark/Moto_Classification_v2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2fed2d634b9ab20e2de8a279dd15ac51b94d81457b7914d11f69d0bac15497c9
- Size of remote file:
- 343 MB
- SHA256:
- 5272225f1b2ad8a42db0c2acd6fa16c125b06b53efcd111e1edba9bd037fb6c3
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