Instructions to use hf-tiny-model-private/tiny-random-ConvNextForImageClassification 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-ConvNextForImageClassification 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-ConvNextForImageClassification") 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-ConvNextForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-tiny-model-private/tiny-random-ConvNextForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- beacfb75d684a6f04c694efc027cdcbc5466332de7719060d59023ef51d0cbb2
- Size of remote file:
- 322 kB
- SHA256:
- cdd6c977c8f27eb4d8569e7a69acf6c32f99512d22846ff8b569d5a7d04c62de
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