Instructions to use jays009/Restnet50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jays009/Restnet50 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="jays009/Restnet50") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jays009/Restnet50", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "model_type": "resnet", | |
| "task": "image-classification", | |
| "framework": "pytorch", | |
| "pipeline_tag": "image-classification", | |
| "preprocessing": { | |
| "resize": 256, | |
| "center_crop": 224, | |
| "normalize": [0.485, 0.456, 0.406], | |
| "normalize_std": [0.229, 0.224, 0.225] | |
| }, | |
| "license": "apache-2.0", | |
| "tags": [ | |
| "image-classification", | |
| "wheat-disease-detection", | |
| "agriculture" | |
| ] | |
| } | |