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") - Notebooks
- Google Colab
- Kaggle
What does the output mean?
#2
by bridger217 - opened
What do the output numbers mean? For example, when I convert it to a coreml model I get:
["linear_72"]
Float32 1 × 4 matrix
[-0.175293,-0.05935669,0.3181152,-0.1418457]
["linear_72"]
["linear_72"]
Float32 1 × 4 matrix
[-0.1253662,0.3828125,-0.03640747,-0.0949707]
["linear_72"]
["linear_72"]
Float32 1 × 4 matrix
[-0.1195068,0.3530273,-0.04281616,-0.1028442]
["linear_72"]
["linear_72"]
Float32 1 × 4 matrix
[-0.07275391,0.3669434,-0.06530762,-0.1351318]
["linear_72"]