Instructions to use jvadlamudi2/convnext-tiny-224-jvadlamudi2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jvadlamudi2/convnext-tiny-224-jvadlamudi2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="jvadlamudi2/convnext-tiny-224-jvadlamudi2") 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("jvadlamudi2/convnext-tiny-224-jvadlamudi2") model = AutoModelForImageClassification.from_pretrained("jvadlamudi2/convnext-tiny-224-jvadlamudi2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 99645ebe15dd13c7ef01bc8d7350248cf256c5028f4bc8e37256aac5e9d8a285
- Size of remote file:
- 4.03 kB
- SHA256:
- 3a5d65f11c65e54072d6ff11dfa5a3e5e57bf83e030a68324205cfdd98539c1a
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