Instructions to use ProbeX/Model-J__ResNet__model_idx_0101 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_0101 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_0101") 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_0101") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0101", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 928853a7764359dd21fe03253cfbe73331580f06730e3ff866c7eacdffa90d3f
- Size of remote file:
- 171 MB
- SHA256:
- c8a07e41c5dba5652846edaab100acd4f45e5921153aefeaa4d4b7b970ea763f
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