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:
- c4135f83e4262064e750a8c391092db10b31cbe905e16c00b89449676f480851
- Size of remote file:
- 49.6 MB
- SHA256:
- f3dbf889867d9fc5b604e23a0d7abe08e579cb5d36d830d09b7f4e1d77297826
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