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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f177836e4fcb7dae85aad7d05c3547f93358515c27c922f40b5adc8a5c83e730
- Size of remote file:
- 442 kB
- SHA256:
- d2ebd5fe8b8da4e0f26d0d516c2e337c64d115fe49d58b13d18fc680f6c0b1e4
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