Datasetpublic

Masutarisu/car-parts-video-detections

Car Parts Video Detections Detection index for a car exterior review video. One row per detected car part per frame. Video: YouTube — YcvECxtXoxQDetector: YOLOv8n-seg fine-tuned on carparts-segSampling: 1 fps Schema Column Type Description video_id string YouTube video ID frame_index int Frame number (1-based) timestamp float Seconds into video class_label string Part name (e.g. front_bumper) class_id int Class number bbox_x_min, bbox_y_min… See the full description on the dataset page: https://huggingface.co/datasets/Masutarisu/car-parts-video-detections.

sourceHugging Facemitupdated 7mo agoView on Hugging Face

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Dataset Card

Car Parts Video Detections

Detection index for a car exterior review video. One row per detected car part per frame.

Video: YouTube — YcvECxtXoxQ Detector: YOLOv8n-seg fine-tuned on carparts-seg Sampling: 1 fps

Schema

Column	Type	Description
video_id	string	YouTube video ID
frame_index	int	Frame number (1-based)
timestamp	float	Seconds into video
class_label	string	Part name (e.g. front_bumper)
class_id	int	Class number
bboxxmin, bboxymin, bboxxmax, bboxymax	float	Bounding box in pixels
confidence_score	float	Detection confidence
detector_name	string	Model used
framewidth, frameheight	int	Frame dimensions

Classes (18)

backbumper, backglass, backleftdoor, backleftlight, backrightdoor, backrightlight, frontbumper, frontglass, frontleftdoor, frontleftlight, frontrightdoor, frontrightlight, hood, leftmirror, rightmirror, tailgate, trunk, wheel

Usage

python

import pandas as pd
df = pd.read_parquet("detections.parquet")
hood = df[df["class_label"] == "hood"]
print(sorted(hood["timestamp"].unique()))