Instructions to use ksukrit/convnextv2-base-22k-224-finetuned-hand_class with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ksukrit/convnextv2-base-22k-224-finetuned-hand_class with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ksukrit/convnextv2-base-22k-224-finetuned-hand_class") 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("ksukrit/convnextv2-base-22k-224-finetuned-hand_class") model = AutoModelForImageClassification.from_pretrained("ksukrit/convnextv2-base-22k-224-finetuned-hand_class", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ea89e03c09bfb67c4335b54be16ffafd42bdc75fd913c7939296713b21395cc5
- Size of remote file:
- 351 MB
- SHA256:
- 150cbb39ea5f8a44b3e4ccfcf1610275a0344dd5b28c6cd102ab36c545b5027d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.