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")# pip install -U transformers accelerate # 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
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license: mit
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license: mit
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datasets:
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- zh-plus/tiny-imagenet
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pipeline_tag: image-classification
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library_name: transformers
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tags:
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- Squeeze
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- New
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- Image
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- Clf
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