Instructions to use cringgaard/ResNet18_Ballast-Type_baseline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cringgaard/ResNet18_Ballast-Type_baseline with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="cringgaard/ResNet18_Ballast-Type_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_Ballast-Type_baseline") model = AutoModelForImageClassification.from_pretrained("cringgaard/ResNet18_Ballast-Type_baseline") - Notebooks
- Google Colab
- Kaggle
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-classification", model="cringgaard/ResNet18_Ballast-Type_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_Ballast-Type_baseline")
model = AutoModelForImageClassification.from_pretrained("cringgaard/ResNet18_Ballast-Type_baseline")Quick Links
# Gated model: Login with a HF token with gated access permission hf auth login