Instructions to use facebook/convnextv2-tiny-1k-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/convnextv2-tiny-1k-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="facebook/convnextv2-tiny-1k-224") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("facebook/convnextv2-tiny-1k-224") model = AutoModelForImageClassification.from_pretrained("facebook/convnextv2-tiny-1k-224", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit History
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Add TF weights
Model converted by the [`transformers`' `pt_to_tf` CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). All converted model outputs and hidden layers were validated against its PyTorch counterpart. Maximum crossload output difference=1.907e-05; Maximum crossload hidden layer difference=7.904e-03; Maximum conversion output difference=1.907e-05; Maximum conversion hidden layer difference=7.904e-03; CAUTION: The maximum admissible error was manually increased to 0.1!42d7c6f
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Update README.md
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Create README.md
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Update config.json
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Upload processor
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Upload ConvNextV2ForImageClassification
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initial commit
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