Instructions to use hf-internal-testing/tiny-random-PvtV2ForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-PvtV2ForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-PvtV2ForImageClassification") 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-PvtV2ForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-PvtV2ForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9cfe6cfd578906d4dd3482c338b05d747b65131d225dd753d1545907ad71e3e6
- Size of remote file:
- 3.11 MB
- SHA256:
- e70fe05ae6ac4b91396f0216ec260bff3ba8489c561cf782fc8696139e21ecc9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.