Instructions to use hf-tiny-model-private/tiny-random-ConvNextV2ForImageClassification 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-ConvNextV2ForImageClassification 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-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-tiny-model-private/tiny-random-ConvNextV2ForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-tiny-model-private/tiny-random-ConvNextV2ForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0e769d52d7060629554dc11e30895c03c1f9d33ece36daf0e49fda4dc3998be1
- Size of remote file:
- 330 kB
- SHA256:
- 3d99e933393d45c51acc5a71c3d1e3d0f014b414cda81926cb0a7db6087fd176
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