Instructions to use BeckerAnas/avid-sponge-222 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BeckerAnas/avid-sponge-222 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="BeckerAnas/avid-sponge-222") 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("BeckerAnas/avid-sponge-222") model = AutoModelForImageClassification.from_pretrained("BeckerAnas/avid-sponge-222", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e69952572f59b9e60ac7d7985831025f9b5e0e66b7d3a4ff06bc5a76bdc4ba31
- Size of remote file:
- 5.71 kB
- SHA256:
- 3b1e11f894c24c7660f970d703287c2ce769b38f50f05219e169950e3d7c8177
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.