Instructions to use ProbeX/Model-J__ResNet__model_idx_0680 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_0680 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_0680") 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_0680") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0680", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e50aea2268903a2a3e3074b581ced9d38e21275750042cda29b0a701df11bc71
- Size of remote file:
- 171 MB
- SHA256:
- 379d339b81c1e21fdcc685ccd2ed5d2ce94a84f1b5102eb187bcbc1200787e9b
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