Instructions to use ProbeX/Model-J__ResNet__model_idx_0058 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_0058 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_0058") 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_0058") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0058") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3bc57bf66b2d35603d8f904302bccc887db0dc0c65f5a483a862c9c0f2fa9527
- Size of remote file:
- 5.37 kB
- SHA256:
- bd37f4ca3e86ba929aa3ee5b05fa7d5adb3297fa6fe73aca4ad9d384e85a9a57
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