Instructions to use ProbeX/Model-J__ResNet__model_idx_0964 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_0964 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_0964") 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_0964") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0964", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 73f69653a532b48252069d149205d4e8722268a57455d2a29fbe0a0e74dcc2ce
- Size of remote file:
- 5.37 kB
- SHA256:
- d1ecfffe7e8cc1aca7e4f9def6285975f3c74d2d723bfaa404bbe5b1852aa194
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.