Instructions to use nwirandx/medicalnet-resnet3d50-23datasets with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nwirandx/medicalnet-resnet3d50-23datasets with Transformers:
# Load model directly from transformers import ResNet3D50ForImageClassification model = ResNet3D50ForImageClassification.from_pretrained("nwirandx/medicalnet-resnet3d50-23datasets", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "ResNet3D50ForImageClassification" | |
| ], | |
| "auto_map": { | |
| "AutoBackbone": "modeling_resnet.ResNet3DBackbone", | |
| "AutoConfig": "configuration_resnet.ResNet3DConfig", | |
| "AutoModel": "modeling_resnet.ResNet3DModel", | |
| "AutoModelForImageClassification": "modeling_resnet.ResNet3DForImageClassification" | |
| }, | |
| "conv1_kernel_size": 7, | |
| "conv1_stride": 2, | |
| "depths": [ | |
| 3, | |
| 4, | |
| 6, | |
| 3 | |
| ], | |
| "downsample_in_bottleneck": false, | |
| "downsample_in_first_stage": false, | |
| "dtype": "float32", | |
| "embedding_size": 64, | |
| "hidden_act": "relu", | |
| "hidden_sizes": [ | |
| 64, | |
| 128, | |
| 256, | |
| 512 | |
| ], | |
| "layer_type": "bottleneck", | |
| "model_type": "resnet3d", | |
| "no_max_pool": false, | |
| "num_channels": 1, | |
| "spatial_dims": 3, | |
| "transformers_version": "4.57.1", | |
| "widen_factor": 1.0 | |
| } | |