Rickkosse/rfdetr-flitsfoto-detector
Object Detection • Updated
Datasets:
image imagewidth (px) 333 640 | image_id int64 532 576k | file_name stringlengths 16 16 | width int32 333 640 | height int32 279 640 | objects dict |
|---|---|---|---|---|---|
16,346 | 000000016346.jpg | 500 | 325 | {
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15,379 | 000000015379.jpg | 500 | 375 | {
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27,942 | 000000027942.jpg | 500 | 358 | {
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28,333 | 000000028333.jpg | 640 | 427 | {
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37,322 | 000000037322.jpg | 640 | 427 | {
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8,836 | 000000008836.jpg | 640 | 480 | {
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32,533 | 000000032533.jpg | 634 | 640 | {
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44,801 | 000000044801.jpg | 500 | 333 | {
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12,650 | 000000012650.jpg | 640 | 480 | {
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34,971 | 000000034971.jpg | 500 | 375 | {
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14,849 | 000000014849.jpg | 640 | 428 | {
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16,027 | 000000016027.jpg | 640 | 451 | {
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} | |
1,799 | 000000001799.jpg | 640 | 480 | {
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0
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252
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} | |
537,379 | 000000537379.jpg | 640 | 480 | {
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16,418 | 000000016418.jpg | 612 | 612 | {
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27,919 | 000000027919.jpg | 501 | 640 | {
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36,939 | 000000036939.jpg | 640 | 479 | {
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5,107 | 000000005107.jpg | 640 | 480 | {
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17,817 | 000000017817.jpg | 640 | 480 | {
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0
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24,522 | 000000024522.jpg | 640 | 466 | {
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26,319 | 000000026319.jpg | 640 | 500 | {
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5,811 | 000000005811.jpg | 640 | 467 | {
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6,270 | 000000006270.jpg | 640 | 480 | {
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37,062 | 000000037062.jpg | 640 | 412 | {
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730 | 000000000730.jpg | 640 | 428 | {
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14,698 | 000000014698.jpg | 640 | 461 | {
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"score": [
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3,982 | 000000003982.jpg | 640 | 426 | {
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21,751 | 000000021751.jpg | 458 | 640 | {
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30,787 | 000000030787.jpg | 640 | 426 | {
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18,882 | 000000018882.jpg | 480 | 640 | {
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36,639 | 000000036639.jpg | 640 | 425 | {
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21,459 | 000000021459.jpg | 480 | 640 | {
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21,253 | 000000021253.jpg | 640 | 332 | {
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27,796 | 000000027796.jpg | 640 | 455 | {
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25,069 | 000000025069.jpg | 500 | 375 | {
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28,560 | 000000028560.jpg | 640 | 427 | {
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8,564 | 000000008564.jpg | 640 | 427 | {
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8,193 | 000000008193.jpg | 640 | 449 | {
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218,106 | 000000218106.jpg | 640 | 480 | {
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23,203 | 000000023203.jpg | 640 | 427 | {
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244,986 | 000000244986.jpg | 640 | 427 | {
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5,965 | 000000005965.jpg | 640 | 480 | {
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5,294 | 000000005294.jpg | 640 | 413 | {
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1,720 | 000000001720.jpg | 640 | 426 | {
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9,018 | 000000009018.jpg | 640 | 431 | {
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25,759 | 000000025759.jpg | 640 | 512 | {
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2,337 | 000000002337.jpg | 333 | 500 | {
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32,832 | 000000032832.jpg | 467 | 640 | {
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22,274 | 000000022274.jpg | 640 | 427 | {
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15,610 | 000000015610.jpg | 640 | 480 | {
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18,214 | 000000018214.jpg | 640 | 480 | {
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39,078 | 000000039078.jpg | 640 | 427 | {
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12,131 | 000000012131.jpg | 640 | 435 | {
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10,434 | 000000010434.jpg | 640 | 426 | {
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38,097 | 000000038097.jpg | 426 | 640 | {
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40,286 | 000000040286.jpg | 640 | 480 | {
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17,244 | 000000017244.jpg | 427 | 640 | {
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28,480 | 000000028480.jpg | 640 | 478 | {
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10,925 | 000000010925.jpg | 640 | 424 | {
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26,747 | 000000026747.jpg | 426 | 640 | {
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29,915 | 000000029915.jpg | 640 | 480 | {
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4,355 | 000000004355.jpg | 640 | 427 | {
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1,315 | 000000001315.jpg | 480 | 640 | {
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31,335 | 000000031335.jpg | 640 | 426 | {
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12,230 | 000000012230.jpg | 640 | 480 | {
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26,274 | 000000026274.jpg | 640 | 480 | {
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35,844 | 000000035844.jpg | 640 | 480 | {
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27,005 | 000000027005.jpg | 640 | 360 | {
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End of preview. Expand in Data Studio
COCO License Plate Pseudo-Labels
Pseudo-gelabelde kentekenplaat bounding boxes op COCO 2017 train-afbeeldingen.
Gegenereerd met open-image-models YOLOv9 platedetector (MIT licentie), toegepast op voertuig-ROIs uit de COCO annotaties.
Dataset structuur
| Kolom | Type | Omschrijving |
|---|---|---|
image |
PIL Image | Originele COCO afbeelding |
image_id |
int | Originele COCO image ID |
file_name |
str | Originele bestandsnaam |
objects |
dict | Bounding boxes + labels + scores |
objects bevat:
bbox: lijst van[x, y, w, h]in pixels (COCO formaat)category: altijd0(license_plate)score: detector confidence (0.0 – 1.0)area: oppervlakte van de box in pixels²
Gebruik
from datasets import load_dataset
ds = load_dataset("Rickkosse/coco-license-plate-pseudo-labels")
sample = ds["train"][0]
print(sample["objects"])
# {'bbox': [[261, 203, 93, 28]], 'category': [0], 'score': [0.87], 'area': [2604]}
Generatie
- Detector:
yolo-v9-t-384-license-plate-end2end(open-image-models, MIT) - Brondata: COCO 2017 train, gefilterd op voertuig-annotaties
- Methode: Detectie per voertuig-ROI met NMS post-processing
- Confidence drempel: 0.4
- Licentie bronafbeeldingen: COCO — CC BY 4.0
Licentie
CC BY 4.0 — vrij te gebruiken voor training, ook commercieel.
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Models trained or fine-tuned on Rickkosse/coco-license-plate-pseudo-labels