Instructions to use ThongCoder/microsoft-resnet50-cifar100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ThongCoder/microsoft-resnet50-cifar100 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ThongCoder/microsoft-resnet50-cifar100") 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("ThongCoder/microsoft-resnet50-cifar100") model = AutoModelForImageClassification.from_pretrained("ThongCoder/microsoft-resnet50-cifar100") - Notebooks
- Google Colab
- Kaggle
metadata
tags:
- autotrain
- transformers
- image-classification
base_model: microsoft/resnet-50
widget:
- src: >-
https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: >-
https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: >-
https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
datasets:
- uoft-cs/cifar100
Model Trained Using AutoTrain
- Problem type: Image Classification
Validation Metrics
No validation metrics available