Instructions to use yashshinde0080/regnet_y_400mf-mlxim-finetuned-chest_xray-pneumonia with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yashshinde0080/regnet_y_400mf-mlxim-finetuned-chest_xray-pneumonia with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="yashshinde0080/regnet_y_400mf-mlxim-finetuned-chest_xray-pneumonia") 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("yashshinde0080/regnet_y_400mf-mlxim-finetuned-chest_xray-pneumonia") model = AutoModelForImageClassification.from_pretrained("yashshinde0080/regnet_y_400mf-mlxim-finetuned-chest_xray-pneumonia", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "RegNetForImageClassification" | |
| ], | |
| "depths": [ | |
| 2, | |
| 6, | |
| 12, | |
| 2 | |
| ], | |
| "downsample_in_first_stage": true, | |
| "dtype": "float32", | |
| "embedding_size": 32, | |
| "groups_width": 64, | |
| "hidden_act": "relu", | |
| "hidden_sizes": [ | |
| 128, | |
| 192, | |
| 512, | |
| 1088 | |
| ], | |
| "id2label": { | |
| "0": "NORMAL", | |
| "1": "PNEUMONIA" | |
| }, | |
| "label2id": { | |
| "NORMAL": 0, | |
| "PNEUMONIA": 1 | |
| }, | |
| "layer_type": "y", | |
| "model_type": "regnet", | |
| "num_channels": 3, | |
| "problem_type": "single_label_classification", | |
| "transformers_version": "4.57.1" | |
| } | |