Instructions to use hamaadayubkhan/NuttriScan with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hamaadayubkhan/NuttriScan with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hamaadayubkhan/NuttriScan") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("hamaadayubkhan/NuttriScan") model = AutoModelForImageClassification.from_pretrained("hamaadayubkhan/NuttriScan", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from hamaadayubkhan/NuttriScan: direct link, hf CLI and curl.
- Browser
- Download file 1.78 kB
-
https://huggingface.co/hamaadayubkhan/NuttriScan/resolve/main/config.json
- Command line
-
hf download hf://hamaadayubkhan/NuttriScan/config.json
-
curl -L -o config.json https://huggingface.co/hamaadayubkhan/NuttriScan/resolve/main/config.json
1.78 kB
| { | |
| "_name_or_path": "microsoft/resnet-50", | |
| "architectures": [ | |
| "ResNetForImageClassification" | |
| ], | |
| "depths": [ | |
| 3, | |
| 4, | |
| 6, | |
| 3 | |
| ], | |
| "downsample_in_bottleneck": false, | |
| "downsample_in_first_stage": false, | |
| "embedding_size": 64, | |
| "hidden_act": "relu", | |
| "hidden_sizes": [ | |
| 256, | |
| 512, | |
| 1024, | |
| 2048 | |
| ], | |
| "id2label": { | |
| "0": "LABEL_0", | |
| "1": "LABEL_1", | |
| "2": "LABEL_2", | |
| "3": "LABEL_3", | |
| "4": "LABEL_4", | |
| "5": "LABEL_5", | |
| "6": "LABEL_6", | |
| "7": "LABEL_7", | |
| "8": "LABEL_8", | |
| "9": "LABEL_9", | |
| "10": "LABEL_10", | |
| "11": "LABEL_11", | |
| "12": "LABEL_12", | |
| "13": "LABEL_13", | |
| "14": "LABEL_14", | |
| "15": "LABEL_15", | |
| "16": "LABEL_16", | |
| "17": "LABEL_17", | |
| "18": "LABEL_18", | |
| "19": "LABEL_19", | |
| "20": "LABEL_20", | |
| "21": "LABEL_21", | |
| "22": "LABEL_22", | |
| "23": "LABEL_23", | |
| "24": "LABEL_24", | |
| "25": "LABEL_25" | |
| }, | |
| "label2id": { | |
| "LABEL_0": 0, | |
| "LABEL_1": 1, | |
| "LABEL_10": 10, | |
| "LABEL_11": 11, | |
| "LABEL_12": 12, | |
| "LABEL_13": 13, | |
| "LABEL_14": 14, | |
| "LABEL_15": 15, | |
| "LABEL_16": 16, | |
| "LABEL_17": 17, | |
| "LABEL_18": 18, | |
| "LABEL_19": 19, | |
| "LABEL_2": 2, | |
| "LABEL_20": 20, | |
| "LABEL_21": 21, | |
| "LABEL_22": 22, | |
| "LABEL_23": 23, | |
| "LABEL_24": 24, | |
| "LABEL_25": 25, | |
| "LABEL_3": 3, | |
| "LABEL_4": 4, | |
| "LABEL_5": 5, | |
| "LABEL_6": 6, | |
| "LABEL_7": 7, | |
| "LABEL_8": 8, | |
| "LABEL_9": 9 | |
| }, | |
| "layer_type": "bottleneck", | |
| "model_type": "resnet", | |
| "num_channels": 3, | |
| "out_features": [ | |
| "stage4" | |
| ], | |
| "out_indices": [ | |
| 4 | |
| ], | |
| "problem_type": "single_label_classification", | |
| "stage_names": [ | |
| "stem", | |
| "stage1", | |
| "stage2", | |
| "stage3", | |
| "stage4" | |
| ], | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.46.2" | |
| } | |