Instructions to use ProbeX/Model-J__ResNet__model_idx_0956 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_0956 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_0956") 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_0956") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0956") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ef0601bf0b9ef5f23346152781173111e644501cba7f584922604d0ee7737820
- Size of remote file:
- 5.37 kB
- SHA256:
- a61e63b4800b74b50fdeb29190d033c7c7801cfe7f71e44b4c543dcf23f5a897
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.