Datasets:

projectsidewalk
/

rampnet-dataset

@@ -42,12 +42,12 @@ print(f"Curb Ramp Points (unnormalized): {unnormalized_points}")
 | **Open Government Datasets** | The initial source of curb ramp locations (<lat, long> coordinates) from 3 US cities (NYC, Portland, Bend) with "Good" location precision. Used as input for Stage 1. | N/A (Geo-data) | 276,615¹ |
 | **Project Sidewalk Crop Pre-training Set** | A subset of Project Sidewalk data used to initially pre-train the crop-level model in Stage 1, which identifies curb ramps within a small, directional image crop. Can be downloaded with `stage_one/crop_model/ps_model/data/download_data.py` | 20,698 | 27,704 |
 | [**Manual Crop Model Training Set**](https://huggingface.co/datasets/projectsidewalk/rampnet-crop-model-dataset) | A small, fully and manually labeled dataset used for a second round of training on the crop-level model to improve its precision and recall. | 312 | 1,212 |
-| __[**RampNet Stage 1 Dataset (Final Output)**](https://huggingface.co/datasets/projectsidewalk/rampnet-dataset)__ | The main, large-scale dataset generated by the Stage 1 auto-translation pipeline, containing curb ramp pixel coordinates on GSV panoramas. This is the primary dataset contribution. | 214,376 | 849,895 |
 | **Manual Ground Truth Set (1k Panos)** | A set of 1,000 panoramas randomly sampled and then fully and manually labeled. This serves as the "gold standard" for evaluating both Stage 1 and Stage 2 performance. Images are included in the Stage 1 Dataset on Hugging Face, but the labels themselves are in `manual_labels`. | 1,000 | 3,919 |
 ¹This number is the sum of curb ramp locations from the three cities with "Good" location precision listed in Table 1: New York City (217,680), Portland (45,324), and Bend (13,611).
-This HF repo is for __[**RampNet Stage 1 Dataset (Final Output)**](https://huggingface.co/datasets/projectsidewalk/rampnet-dataset)__!
 ## Citation

 | **Open Government Datasets** | The initial source of curb ramp locations (<lat, long> coordinates) from 3 US cities (NYC, Portland, Bend) with "Good" location precision. Used as input for Stage 1. | N/A (Geo-data) | 276,615¹ |
 | **Project Sidewalk Crop Pre-training Set** | A subset of Project Sidewalk data used to initially pre-train the crop-level model in Stage 1, which identifies curb ramps within a small, directional image crop. Can be downloaded with `stage_one/crop_model/ps_model/data/download_data.py` | 20,698 | 27,704 |
 | [**Manual Crop Model Training Set**](https://huggingface.co/datasets/projectsidewalk/rampnet-crop-model-dataset) | A small, fully and manually labeled dataset used for a second round of training on the crop-level model to improve its precision and recall. | 312 | 1,212 |
+| [**⭐ RampNet Stage 1 Dataset (Final Output)**](https://huggingface.co/datasets/projectsidewalk/rampnet-dataset) | The main, large-scale dataset generated by the Stage 1 auto-translation pipeline, containing curb ramp pixel coordinates on GSV panoramas. This is the primary dataset contribution. | 214,376 | 849,895 |
 | **Manual Ground Truth Set (1k Panos)** | A set of 1,000 panoramas randomly sampled and then fully and manually labeled. This serves as the "gold standard" for evaluating both Stage 1 and Stage 2 performance. Images are included in the Stage 1 Dataset on Hugging Face, but the labels themselves are in `manual_labels`. | 1,000 | 3,919 |
 ¹This number is the sum of curb ramp locations from the three cities with "Good" location precision listed in Table 1: New York City (217,680), Portland (45,324), and Bend (13,611).
+This HF repo is for [**⭐ RampNet Stage 1 Dataset (Final Output)**](https://huggingface.co/datasets/projectsidewalk/rampnet-dataset)!
 ## Citation