Instructions to use Am22000/classifier_image with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Am22000/classifier_image with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Am22000/classifier_image") 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("Am22000/classifier_image") model = AutoModelForImageClassification.from_pretrained("Am22000/classifier_image", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2ac16f81f13b277f6d28a294de59bb6020df6dfcf03a7d98046372323d0b540d
- Size of remote file:
- 343 MB
- SHA256:
- 9677af706029a1b79dd1f7e58e84059682bf1f136bb1b30636a4cadf4f8be728
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