Instructions to use prithivMLmods/Mirage-Photo-Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Mirage-Photo-Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/Mirage-Photo-Classifier") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("prithivMLmods/Mirage-Photo-Classifier") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/Mirage-Photo-Classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7c10fc6beac9a55fde5442810690a9168c58ec371d9ed8aa1631cd80351ee45c
- Size of remote file:
- 372 MB
- SHA256:
- 35a4b9cb98afd93afcf279fae9b1aaf2efdc5355ff43e93aeb602c9bef55ce97
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.