Instructions to use AmitMidday/Dogs-Breed-Classification-Using-Vision-Transformers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AmitMidday/Dogs-Breed-Classification-Using-Vision-Transformers with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="AmitMidday/Dogs-Breed-Classification-Using-Vision-Transformers") 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("AmitMidday/Dogs-Breed-Classification-Using-Vision-Transformers") model = AutoModelForImageClassification.from_pretrained("AmitMidday/Dogs-Breed-Classification-Using-Vision-Transformers") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5cb100f55c4d02db6d80a31ea75e7737a71286124a3c3f1c349ad3e69fad5d0c
- Size of remote file:
- 3.58 kB
- SHA256:
- 5fb3223a1f107acb1ce8859f9f0620b919f98ce478190ae0be8a7b5f2e134941
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.