Instructions to use hf-internal-testing/tiny-random-ResNetForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-ResNetForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-ResNetForImageClassification") 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("hf-internal-testing/tiny-random-ResNetForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-ResNetForImageClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 131 Bytes
34a180a | 1 2 3 4 | version https://git-lfs.github.com/spec/v1
oid sha256:a359d4e86e837eb5751aed776669c747d91530fb7fb2ca8c29a5aeef4fefc77d
size 203224
|