Instructions to use hf-internal-testing/tiny-random-ConvNextForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-ConvNextForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/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-internal-testing/tiny-random-ConvNextForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-ConvNextForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7713958d30bfd5c422ddcf499e7cf38a749766e03f7de8885f47942ea04246e9
- Size of remote file:
- 423 kB
- SHA256:
- ac631c348e86c6ee6f9f24e517d88af84b615791bae5ef25fb957f5f2c2146f3
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