Instructions to use karim155/convnext-tiny-224-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karim155/convnext-tiny-224-finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="karim155/convnext-tiny-224-finetuned") 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("karim155/convnext-tiny-224-finetuned") model = AutoModelForImageClassification.from_pretrained("karim155/convnext-tiny-224-finetuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
convnext-tiny-224-finetuned / runs /Sep04_19-03-43_93e6f912ca7d /events.out.tfevents.1725476631.93e6f912ca7d.1732.2
- Xet hash:
- c96fe6eadaa7992a78726a14ad624762530c9618d14b68393e68cd5f99015e92
- Size of remote file:
- 15.4 kB
- SHA256:
- 077e13a9e698e074e21684e3652a34cc8fa8fe0eefe8389292c3043b630c3bcd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.