Instructions to use ahmedesmail16/Project_Class_Aug_Model_vit-large-patch16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ahmedesmail16/Project_Class_Aug_Model_vit-large-patch16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ahmedesmail16/Project_Class_Aug_Model_vit-large-patch16") 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("ahmedesmail16/Project_Class_Aug_Model_vit-large-patch16") model = AutoModelForImageClassification.from_pretrained("ahmedesmail16/Project_Class_Aug_Model_vit-large-patch16") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9def04d1bc36fed8f6e84601c5e8377bac209ce0361372c6764cd3d0d80965f5
- Size of remote file:
- 1.21 GB
- SHA256:
- d46357e07766dc41488c2ea7ae0963141731a1afd544d9470d2d9529e10a3e6c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.