Instructions to use ProbeX/Model-J__ResNet__model_idx_0036 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProbeX/Model-J__ResNet__model_idx_0036 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProbeX/Model-J__ResNet__model_idx_0036") 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("ProbeX/Model-J__ResNet__model_idx_0036") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0036") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c027eccc0b4a1ca45a89fcfbca080dc36185964b1e066f16588de51b3a36fe69
- Size of remote file:
- 5.37 kB
- SHA256:
- 6fcd33c5eba94418d369745b170ec406691c7b4bd4d7112d6304aa906dd84cd0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.