Instructions to use siddheshtv/BlockNet10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use siddheshtv/BlockNet10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="siddheshtv/BlockNet10") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import BlockNet10 model = BlockNet10.from_pretrained("siddheshtv/BlockNet10", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d06425cc5869b80a7d6dfff05db7b4400ebe8e80e789635071018a7b8f7b8e9c
- Size of remote file:
- 80.8 MB
- SHA256:
- 7be1849972498ca04e513cf1cf3a49d626f011f0ba337a404412791c810d0df4
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