Instructions to use ProbeX/Model-J__ResNet__model_idx_0199 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_0199 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_0199") 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_0199") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0199", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d4d26da719fc2b821da01409ea7400bc5e9f00d4175c726ba024cf6c343d745b
- Size of remote file:
- 5.37 kB
- SHA256:
- a62a6c1cc497d290ab1ffa6340f39895af1ff218902f646de3702a2eb4f04a83
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.