Instructions to use hf-internal-testing/tiny-random-PvtForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-PvtForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-PvtForImageClassification") 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-PvtForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-PvtForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d5c26b77282125fdd9cf32fa3fc33b7024c9edbc42d9fb84564aa7402bb67879
- Size of remote file:
- 3.38 MB
- SHA256:
- 3e27f571a0744cba30a0e8990474f02ffd8440f71465335f7af32b61d8495199
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.