Instructions to use cquentin48/deep_learning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cquentin48/deep_learning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="cquentin48/deep_learning") 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("cquentin48/deep_learning") model = AutoModelForImageClassification.from_pretrained("cquentin48/deep_learning") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cdb8d44357af0ec579f51fb38f443d298011514e26c75c154559fd500dd3b8cc
- Size of remote file:
- 103 MB
- SHA256:
- ebcf6cc395f16cb38d8b29a865879a9207505373d09830757e17ee36e0d4df91
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