Instructions to use hf-tiny-model-private/tiny-random-Swinv2ForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-Swinv2ForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-tiny-model-private/tiny-random-Swinv2ForImageClassification") 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-tiny-model-private/tiny-random-Swinv2ForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-tiny-model-private/tiny-random-Swinv2ForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 57997b0414a811ad788acd27e38bf4c49376f806cd99db143ca08518d9c78c80
- Size of remote file:
- 329 kB
- SHA256:
- e30eda749d49c60c42f71fa42587ed2552e20254f52d9c36d957513740a40512
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.