Instructions to use mmanikanta/ResNet_AI_image_detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mmanikanta/ResNet_AI_image_detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="mmanikanta/ResNet_AI_image_detector") 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("mmanikanta/ResNet_AI_image_detector") model = AutoModelForImageClassification.from_pretrained("mmanikanta/ResNet_AI_image_detector") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d342fc90d6f603a5a9ee53e8eed7688d8a5be2d1f1b6ece31ad021d31d7ce43f
- Size of remote file:
- 94.4 MB
- SHA256:
- 5872b1cbd76858f7bb60815cc2d4b77cd692986292725feeab1a64a8ca05b285
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