Instructions to use Dewa/dog_emotion_v3_resnet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dewa/dog_emotion_v3_resnet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Dewa/dog_emotion_v3_resnet") 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_v3_resnet") model = AutoModelForImageClassification.from_pretrained("Dewa/dog_emotion_v3_resnet", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 83993fd3bffa0e04e99c84a7f12f682bef161085a43b4d382cceacfebf4e59e7
- Size of remote file:
- 3.96 kB
- SHA256:
- a0e5b227f6277172850cd8386022267f02200441054dff591a89bd695223ea03
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.