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:
- 9df77cb9bda0e9f23a52ff049cc2664bf380a82918e2502a1bc454dab2f0105a
- Size of remote file:
- 330 kB
- SHA256:
- 895655c9f5ec789f61d7ba583a8b59b2c00a27ce52d72da949baf251eebbb2c9
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