Keypoint Detection
Transformers
Safetensors
rampnet
feature-extraction
curb-ramp-detection
accessibility
street-view
keypoint-heatmap
custom_code
Instructions to use projectsidewalk/rampnet-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use projectsidewalk/rampnet-model with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("projectsidewalk/rampnet-model", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Re-export with transformers-compatible RampNetModel wrapper + corrected card (fixes #19); weights unchanged from v1.0-paper
606a119 verified | from transformers import PretrainedConfig | |
| class RampNetConfig(PretrainedConfig): | |
| model_type = "rampnet" | |
| def __init__( | |
| self, | |
| input_size=(2048, 4096), | |
| heatmap_size=(512, 1024), | |
| image_mean=(0.485, 0.456, 0.406), | |
| image_std=(0.229, 0.224, 0.225), | |
| recommended_threshold=0.55, | |
| recommended_min_distance=10, | |
| tta_recommended=True, | |
| **kwargs, | |
| ): | |
| self.input_size = list(input_size) | |
| self.heatmap_size = list(heatmap_size) | |
| self.image_mean = list(image_mean) | |
| self.image_std = list(image_std) | |
| self.recommended_threshold = recommended_threshold | |
| self.recommended_min_distance = recommended_min_distance | |
| self.tta_recommended = tta_recommended | |
| super().__init__(**kwargs) | |