Instructions to use cringgaard/ResNet18_Designer_baseline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cringgaard/ResNet18_Designer_baseline with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="cringgaard/ResNet18_Designer_baseline") 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("cringgaard/ResNet18_Designer_baseline") model = AutoModelForImageClassification.from_pretrained("cringgaard/ResNet18_Designer_baseline", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fa5a00087930b41a14111f8b9eb8c909586e78f96abae4169f91891dad6ce85b
- Size of remote file:
- 3.5 kB
- SHA256:
- bb698e5b0fd74d72d4207b0695300759f7ab1e24e75eb829bc7e29416f66fd88
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