{
  "$type": "site.standard.document",
  "bskyPostRef": {
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    "uri": "at://did:plc:pgryn3ephfd2xgft23qokfzt/app.bsky.feed.post/3mgtyimlk37i2"
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  "path": "/t/feedback-request-synthetic-dataset-for-pedestrian-detection-in-extreme-night-fog/174215#post_1",
  "publishedAt": "2026-03-12T08:17:54.000Z",
  "site": "https://discuss.huggingface.co",
  "tags": [
    "JTSGRIT/NightFogPedestrianDataset_KiaRayPOV · Datasets at Hugging Face"
  ],
  "textContent": "Hi everyone,\n\nI’ve recently published a synthetic dataset focused on a specific autonomous driving edge case: **Pedestrians in heavy night fog.** As we know, real-world data for extreme weather is often hard to collect and label. To address this, I used **Unity Perception** to simulate a night-time environment from the perspective of a vehicle (specifically a Kia Ray, with a camera height of 1.4m).\n\n**Key Features of this Dataset:**\n\n  * **Environment:** High-density fog with low-light night conditions.\n\n  * **Quantity:** 250 pairs of images and YOLOv8 formatted labels (Sample version).\n\n  * **Perspective:** Realistic dashcam POV (Point of View).\n\n  * **Variety:** Randomized pedestrian placements and fog densities.\n\n\n\n\n**I would highly appreciate your feedback on:**\n\n  1. **Visual Fidelity:** Does the synthetic fog and lighting look realistic enough to bridge the “Sim-to-Real” gap?\n\n  2. **Label Accuracy:** Are the bounding box annotations precise enough for your training pipelines?\n\n  3. **Data Value:** How useful do you find these types of synthetic edge cases for improving model robustness?\n\n\n\n\n**Dataset Link:** JTSGRIT/NightFogPedestrianDataset_KiaRayPOV · Datasets at Hugging Face\n\nI’m planning to expand this dataset to include more weather conditions (snow, heavy rain) and more classes (cyclists, motorcycles). Looking forward to your professional insights!\n\nBest regards,",
  "title": "[Feedback Request] Synthetic Dataset for Pedestrian Detection in Extreme Night Fog"
}