Image Classification
Transformers
TensorBoard
Safetensors
vit
vision
Generated from Trainer
Eval Results (legacy)
Instructions to use amunchet/rorshark-vit-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amunchet/rorshark-vit-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="amunchet/rorshark-vit-base") 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("amunchet/rorshark-vit-base") model = AutoModelForImageClassification.from_pretrained("amunchet/rorshark-vit-base", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e29373e00732f8c6d62d72eb57a20c6e5d6a693e64dc893960a314bea4ef89de
- Size of remote file:
- 4.73 kB
- SHA256:
- ffcc3f59e70812095756ed77abfa7f9b0700337a7c9d25983e9245c2dc331049
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