Instructions to use wangyh6/custom-resnet50d-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wangyh6/custom-resnet50d-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="wangyh6/custom-resnet50d-v1", trust_remote_code=True) 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("wangyh6/custom-resnet50d-v1", trust_remote_code=True) model = AutoModelForImageClassification.from_pretrained("wangyh6/custom-resnet50d-v1", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "ResnetModelForImageClassification" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_resnet.ResnetConfig", | |
| "AutoModelForImageClassification": "modeling_resnet.ResnetModelForImageClassification" | |
| }, | |
| "avg_down": true, | |
| "base_width": 64, | |
| "block_type": "bottleneck", | |
| "cardinality": 1, | |
| "input_channels": 3, | |
| "layers": [ | |
| 3, | |
| 4, | |
| 6, | |
| 3 | |
| ], | |
| "model_type": "resnet", | |
| "num_classes": 1000, | |
| "stem_type": "deep", | |
| "stem_width": 32, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.43.0.dev0" | |
| } | |