Instructions to use ProbeX/Model-J__ResNet__model_idx_0262 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_0262 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_0262") 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_0262") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0262", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 80348e59b224738e19be7afcd510ff742482b9d3f9931ea796aa19064a40584e
- Size of remote file:
- 5.37 kB
- SHA256:
- 8989042e366cd0c91badca7d8e52f9ae9a4039fab8b0e2541ff79b250be3b877
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.