Instructions to use Dewa/dog_emotion_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dewa/dog_emotion_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Dewa/dog_emotion_v2") 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("Dewa/dog_emotion_v2") model = AutoModelForImageClassification.from_pretrained("Dewa/dog_emotion_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- eb543eb19161ca8a4b11fa4610c9bce01bb76c9d1e727d386197fde00c545f77
- Size of remote file:
- 3.58 kB
- SHA256:
- b90c90a6d770c7b974a2d6953f104c18c5c04b57a30a53899dbfacfa4bd8493c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.