Instructions to use ProbeX/Model-J__ResNet__model_idx_0894 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_0894 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_0894") 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_0894") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0894", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 655611918aa65673d6cd09c49035301597298b9959287ce41d176ba2975cf4bc
- Size of remote file:
- 171 MB
- SHA256:
- 127d4666cdef92ddcf8ee20cfed7d9abcc3fb0c20fa35c1521bb0a6ec3786dff
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