Instructions to use ProbeX/Model-J__ResNet__model_idx_0141 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_0141 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_0141") 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_0141") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0141", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6e8d4d9d93e89805bfdb7b334e18f98daa00713ce8704bcbae6f4dda84afba80
- Size of remote file:
- 5.37 kB
- SHA256:
- eadd5f98e9faee1369ec638fbf6f71932f31e6a9dc801bcdf1659de6e896dadc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.