Instructions to use ZafarLocAI/convnext_checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ZafarLocAI/convnext_checkpoints with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ZafarLocAI/convnext_checkpoints") 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("ZafarLocAI/convnext_checkpoints") model = AutoModelForImageClassification.from_pretrained("ZafarLocAI/convnext_checkpoints", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ZafarLocAI/convnext_checkpoints: direct link, hf CLI and curl.
- Browser
- Download file 5.2 kB
-
https://huggingface.co/ZafarLocAI/convnext_checkpoints/resolve/main/training_args.bin
- Command line
-
hf download hf://ZafarLocAI/convnext_checkpoints/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ZafarLocAI/convnext_checkpoints/resolve/main/training_args.bin
5.2 kB
- Xet hash:
- 6307d9eb09cc7fdd7ecde26435cee1c9f9791d27794c404af0822df1f97fbd61
- Size of remote file:
- 5.2 kB
- SHA256:
- 03ec2708d378aa31369a01136b862765a3afffd040a6691809c33659a004b6c8
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