Instructions to use raks87/resnet-18-finetuned-cifar10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use raks87/resnet-18-finetuned-cifar10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="raks87/resnet-18-finetuned-cifar10") 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("raks87/resnet-18-finetuned-cifar10") model = AutoModelForImageClassification.from_pretrained("raks87/resnet-18-finetuned-cifar10", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5afb61e19ccf34c2f73f9f34472bddbee15ce6fb7d0f621678ab47c0e653d490
- Size of remote file:
- 4.92 kB
- SHA256:
- 2f4167c8f1645f5a7b392bc5a060f7d621a63d54f6c3ef6442da78978f73c708
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