Instructions to use mohitsha/resnet-tiny-beans-quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mohitsha/resnet-tiny-beans-quantized with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="mohitsha/resnet-tiny-beans-quantized") 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("mohitsha/resnet-tiny-beans-quantized") model = AutoModelForImageClassification.from_pretrained("mohitsha/resnet-tiny-beans-quantized", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "onnx_model_2", | |
| "architectures": [ | |
| "ResNetForImageClassification" | |
| ], | |
| "depths": [ | |
| 2, | |
| 2 | |
| ], | |
| "downsample_in_first_stage": false, | |
| "embedding_size": 64, | |
| "finetuning_task": "image-classification", | |
| "hidden_act": "relu", | |
| "hidden_sizes": [ | |
| 32, | |
| 64 | |
| ], | |
| "id2label": { | |
| "0": "angular_leaf_spot", | |
| "1": "bean_rust", | |
| "2": "healthy" | |
| }, | |
| "image_size": 224, | |
| "label2id": { | |
| "angular_leaf_spot": "0", | |
| "bean_rust": "1", | |
| "healthy": "2" | |
| }, | |
| "layer_type": "basic", | |
| "model_type": "resnet", | |
| "num_channels": 3, | |
| "out_features": null, | |
| "problem_type": "single_label_classification", | |
| "stage_names": [ | |
| "stem", | |
| "stage1", | |
| "stage2" | |
| ], | |
| "transformers_version": "4.27.4" | |
| } | |