Instructions to use hf-internal-testing/tiny-random-ConvNextV2ForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-ConvNextV2ForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-ConvNextV2ForImageClassification") 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-ConvNextV2ForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-ConvNextV2ForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8a94e523d5afda982f75bf74299d7cff30e093be0c826c60fb5125af3337c6a0
- Size of remote file:
- 330 kB
- SHA256:
- 4d0b7525a179a541c9795dd24e0d21161b0c55ac901c6c9a599d84ea4000bc94
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.