Instructions to use kittendev/visual_emotional_analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kittendev/visual_emotional_analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="kittendev/visual_emotional_analysis") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("kittendev/visual_emotional_analysis") model = AutoModelForImageClassification.from_pretrained("kittendev/visual_emotional_analysis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from kittendev/visual_emotional_analysis: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/kittendev/visual_emotional_analysis/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://kittendev/visual_emotional_analysis/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/kittendev/visual_emotional_analysis/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- d0f81f28e42a262c92e46b7d3567c5706762b00036faad63b1ec9343d2377969
- Size of remote file:
- 343 MB
- SHA256:
- cdbe535c7faa32795961d4b638ff020b55cd61ab629575fb51b1664d9b52869b
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