Instructions to use ProbeX/Model-J__ResNet__model_idx_0193 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_0193 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_0193") 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_0193") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0193", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e127c4f4bb207e5791e721f7dd014d9091d231e2d06c544930b0268c4628ab61
- Size of remote file:
- 5.37 kB
- SHA256:
- b0f6b7b388c04c64de34f799e95a07c73ecee9e162e49b1f3f8ca825d1d791d9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.