Instructions to use ProbeX/Model-J__ResNet__model_idx_0059 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_0059 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_0059") 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_0059") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0059") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4112094e05ad83877730dc67043f7234ebfd9c4b9d81187a67c0c1a9e1aaa0c3
- Size of remote file:
- 5.37 kB
- SHA256:
- 285b3b4bfcbb6a2ba19f43aedafe6bc486588d3132647ca030d8eac8210fc903
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.