Instructions to use kd13/Modern-SqueezeNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kd13/Modern-SqueezeNet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="kd13/Modern-SqueezeNet", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModelForImageClassification model = AutoModelForImageClassification.from_pretrained("kd13/Modern-SqueezeNet", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b165eab24a671448cc0e0b5a77c35c66bd57f90dc1b36449b5f117156a9a1d70
- Size of remote file:
- 7.53 MB
- SHA256:
- 0952dfcbeba2a32e1753d53c21fb239de7b0feb6bbea4445794b2c3dc5dc0061
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