ccczy-czy/rav4-video-detections

RAV4 Video Detection and Retrieval Index

This dataset contains two Parquet files produced by a YOLO26 object detection pipeline applied to a Toyota RAV4 exterior review video. Together they form a complete image-to-video semantic retrieval system.

Source Video

Field	Value
Video ID	YcvECxtXoxQ
YouTube URL	https://www.youtube.com/watch?v=YcvECxtXoxQ
Sampling rate	0.5 fps (one frame every 2 seconds)
Total frames	~1,396

Detector

Field	Value
Model	YOLO26n-seg (nano segmentation variant)
Base weights	yolo26n-seg.pt (COCO pretrained)
Fine-tuning data	Ultralytics Car Parts Segmentation Dataset (carparts-seg.yaml)
Training epochs	50
Image size	640 × 640
Batch size	32
Hardware	NVIDIA A100 80GB (Google Colab)
Confidence cutoff	0.30

Files

File	Description
video_detections.parquet	Detection index — one row per video frame
retrieval_output.parquet	Retrieval results — one row per query × class pair

---

video_detections.parquet Schema

One row per video frame. All bounding-box detections within a frame are grouped as parallel lists, so class_labels[i], confidence_scores[i], x_mins[i], etc. all correspond to the same detection.

Column	Type	Description
video_id	string	YouTube video ID (YcvECxtXoxQ)
frame_index	int32	1-based frame number from ffmpeg output
timestamp_sec	float32	Wall-clock position: frame_index × 2 seconds
detection_count	int32	Number of detections in this frame (0 if none)
class_labels	list[string]	Detected car part names (see class list below)
confidence_scores	list[float32]	YOLO confidence per detection, in [0, 1]
x_mins	list[float32]	Bounding box left edges in pixels
y_mins	list[float32]	Bounding box top edges in pixels
x_maxs	list[float32]	Bounding box right edges in pixels
y_maxs	list[float32]	Bounding box bottom edges in pixels
detector_name	string	Model identifier: "yolo26n-seg-carparts-ft50ep"

---

retrieval_output.parquet Schema

One row per (query image × detected class label) pair. All retrieved video segments for that combination are grouped as parallel lists, so start_timestamps[i], end_timestamps[i], supporting_detections[i], and verification_urls[i] all describe the same segment.

Column	Type	Description
query_timestamp_sec	int32	Source timestamp of the query image in seconds
query_timestamp	string	Human-readable "MM:SS" label
query_class_labels	string	Comma-separated list of all classes detected in query
class_label	string	The class driving this retrieval group
segment_count	int32	Number of contiguous segments found in the video
start_timestamps	list[float32]	Segment start times in the corpus video (seconds)
end_timestamps	list[float32]	Segment end times in the corpus video (seconds)
supporting_detections	list[int32]	Number of supporting frame detections per segment
total_supporting_detections	int32	Sum of supporting_detections across all segments
verification_urls	list[string]	YouTube embed URLs for visual verification per segment

---

Class Labels

The 23 classes come from carparts-seg.yaml:

ID	Label	ID	Label
0	back_bumper	12	front_left_light
1	back_door	13	front_light
2	back_glass	14	front_right_door
3	back_left_door	15	front_right_light
4	back_left_light	16	hood
5	back_light	17	left_mirror
6	back_right_door	18	object
7	back_right_light	19	right_mirror
8	front_bumper	20	tailgate
9	front_door	21	trunk
10	front_glass	22	wheel
11	front_left_door

---

How to Load

import pandas as pd

# Detection index
detections = pd.read_parquet(
    "hf://datasets/ccczy-czy/rav4-video-detections/video_detections.parquet"
)

# Retrieval results
retrieval = pd.read_parquet(
    "hf://datasets/ccczy-czy/rav4-video-detections/retrieval_output.parquet"
)

Example: Find All Segments Where a Class Was Detected

import pandas as pd

retrieval = pd.read_parquet(
    "hf://datasets/ccczy-czy/rav4-video-detections/retrieval_output.parquet"
)

# All retrieval groups for front_left_light
rows = retrieval[retrieval["class_label"] == "front_left_light"]
for _, row in rows.iterrows():
    for start, end, url in zip(
        row["start_timestamps"], row["end_timestamps"], row["verification_urls"]
    ):
        print(f"  {start:.0f}s – {end:.0f}s  {url}")