Instructions to use ProbeX/Model-J__ResNet__model_idx_0743 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_0743 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_0743") 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_0743") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0743", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fbb50f48171d999580f7790209da5da8914550ac2230a524fffd5e881235356f
- Size of remote file:
- 5.37 kB
- SHA256:
- 66773ff08d2f9084d4406dbfaa6739ce30bfef1ce240da43a6f57405527ab5a4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.