Instructions to use jhoppanne/Dogs-Breed-Image-Classification-V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jhoppanne/Dogs-Breed-Image-Classification-V1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="jhoppanne/Dogs-Breed-Image-Classification-V1") 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("jhoppanne/Dogs-Breed-Image-Classification-V1") model = AutoModelForImageClassification.from_pretrained("jhoppanne/Dogs-Breed-Image-Classification-V1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 772680770511905b7729b00e4987358d20d65fe4710a7058b5ee99408a784ae0
- Size of remote file:
- 4.73 kB
- SHA256:
- e589cc674be7319ce8277ff9c33b1121c56d3dd49e0dfdce0076b2f5374b7aed
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.