Instructions to use Ahmed9275/ALL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ahmed9275/ALL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Ahmed9275/ALL") 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("Ahmed9275/ALL") model = AutoModelForImageClassification.from_pretrained("Ahmed9275/ALL", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 171858450e82c60bab9138a049cf01e1ba65220d45ec2811800f63a283b28c61
- Size of remote file:
- 343 MB
- SHA256:
- 5f5545af7216c52f00d35b5d96409c4dad9ae1f4d0c4fdaab33e1a9953129369
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.