Instructions to use Thastp/efficientnet_b1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Thastp/efficientnet_b1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Thastp/efficientnet_b1", trust_remote_code=True) 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("Thastp/efficientnet_b1", trust_remote_code=True) model = AutoModelForImageClassification.from_pretrained("Thastp/efficientnet_b1", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
Upload model
Browse files- config.json +1 -0
- model.safetensors +1 -1
config.json
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"_name_or_path": "./efficientnet/temp",
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model.safetensors
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size 31474952
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