Instructions to use ideepankarsharma2003/AI_ImageClassification_SDXL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ideepankarsharma2003/AI_ImageClassification_SDXL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ideepankarsharma2003/AI_ImageClassification_SDXL") 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("ideepankarsharma2003/AI_ImageClassification_SDXL") model = AutoModelForImageClassification.from_pretrained("ideepankarsharma2003/AI_ImageClassification_SDXL", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4ffb4ba4078afdd4586f09bb1e5c422a145c6911cce2cf4f9c369a5e09a32812
- Size of remote file:
- 347 MB
- SHA256:
- e954f5bf86d21a1c4dede8181ff3e40325351eb4c922f44c1f1bf58a0fcc9a8d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.