Datasets:

Anshulgada
/

RT-PDS

license: mit
task_categories:
  - object-detection
language:
  - en
pretty_name: Real Time Pothole Detection System Training Dataset
size_categories:
  - n<1K
tags:
  - Yolo
  - AI/ML
  - Pothole
  - Ultralytics
  - Object Detection

Real Time Pothole Detection System Training Dataset & Model Files

Model Files

Primary model: pothole-detector.pt — this is the actual pre-trained YOLOv10b model used for this project.

You can download it directly from the Hugging Face Hub:

Direct download link: pothole-detector.pt
Python snippet:

from huggingface_hub import hf_hub_download

model_path = hf_hub_download(
    repo_id="Anshulgada/RT-PDS",
    filename="pothole-detector.pt"
)

Other Available Ultralytics Variants

Model	Description
yolov10n.pt	Nano model, smallest & fastest
yolov10s.pt	Small model
yolov10m.pt	Medium model
yolov10b.pt	Base model
yolov10l.pt	Large model
yolov10x.pt	Extra large, highest accuracy

By default, these Ultralytics weights are available from:

👉 https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov10{variant-name[n,s,m,b,l,x]}.pt

A backup of these models may also be hosted on Hugging Face Hub.

Dataset Structure

The dataset follows the standard YOLO format with separate directories for training, validation, and testing. Each split contains both images/ and labels/ subdirectories with matching filenames.

Yolo/
├── Inference Images/       # Example images for quick testing
└── Datasets/
    ├── train/
    │   ├── images/         # ~38k training images
    │   └── labels/         # YOLO-format labels
    ├── valid/
    │   ├── images/         # 6k validation images
    │   └── labels/         
    └── test/
        ├── images/         # 10k test images
        └── labels/

You can download it directly from the Hugging Face Hub:

Direct download link: Yolo.zip
Python snippet:

from huggingface_hub import hf_hub_download

# Download the zipped YOLO dataset
dataset_path = hf_hub_download(
    repo_id="Anshulgada/RT-PDS",
    filename="Yolo.zip",
    repo_type="dataset"
)

print("Dataset downloaded to:", dataset_path)