Image Classification
Transformers
TensorBoard
Safetensors
PyTorch
vit
huggingpics
Eval Results (legacy)
Instructions to use Libidrave/CartoonOrNotv1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Libidrave/CartoonOrNotv1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Libidrave/CartoonOrNotv1") 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("Libidrave/CartoonOrNotv1") model = AutoModelForImageClassification.from_pretrained("Libidrave/CartoonOrNotv1") - Notebooks
- Google Colab
- Kaggle
Update Readme
Browse files
README.md
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metrics:
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- accuracy
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model-index:
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- name: CartoonOrNotv1
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results:
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name: Image Classification
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type: image-classification
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metrics:
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---
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# CartoonOrNotv1
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- huggingpics
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metrics:
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- accuracy
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model-index:
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- name: CartoonOrNotv1
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results:
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name: Image Classification
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type: image-classification
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.9016393423080444
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license: apache-2.0
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language:
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pipeline_tag: image-classification
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# CartoonOrNotv1
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