Instructions to use AashishKumar/AIvisionGuard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AashishKumar/AIvisionGuard with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="AashishKumar/AIvisionGuard") 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("AashishKumar/AIvisionGuard") model = AutoModelForImageClassification.from_pretrained("AashishKumar/AIvisionGuard", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6470af3da7b0234092ff7e3baf33ebc98a72cd4ee3032d0d20465c94af523806
- Size of remote file:
- 687 MB
- SHA256:
- c168131779fad488e21d66cea932fdf04454c3af0a84dc7208281c1addae7a62
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.