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Scan (1).jpg
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film_damage_simulator
[ { "label": "dust", "bbox": [ 0.11245, 0.894366, 0.114458, 0.8973 ] }, { "label": "dust", "bbox": [ 0.068474, 0.936913, 0.07008, 0.939554 ] }, { "label": "dust", "bbox": [ 0.934538, 0.935739, 0.935944, 0.9...
Scan (10).jpg
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3,336
film_damage_simulator
[ { "label": "dust", "bbox": [ 0.622196, 0.240707, 0.623397, 0.242206 ] }, { "label": "dust", "bbox": [ 0.571715, 0.233513, 0.573718, 0.235911 ] }, { "label": "dust", "bbox": [ 0.571715, 0.572542, 0.573317, ...
Scan (2).jpg
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film_damage_simulator
[{"label":"dust","bbox":[0.898795,0.1875,0.900803,0.190434]},{"label":"dust","bbox":[0.598394,0.6584(...TRUNCATED)
Scan (3).jpg
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film_damage_simulator
[{"label":"dust","bbox":[0.753641,0.159894,0.755866,0.16225]},{"label":"dust","bbox":[0.13754,0.5450(...TRUNCATED)
Scan (4).jpg
4,944
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film_damage_simulator
[{"label":"dust","bbox":[0.870348,0.040342,0.871764,0.042403]},{"label":"dust","bbox":[0.240494,0.06(...TRUNCATED)
Scan (5).jpg
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film_damage_simulator
[{"label":"dust","bbox":[0.62318,0.941696,0.626011,0.944935]},{"label":"dust","bbox":[0.701254,0.839(...TRUNCATED)
Scan (6).jpg
4,944
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film_damage_simulator
[{"label":"dust","bbox":[0.315736,0.087202,0.317759,0.088988]},{"label":"dust","bbox":[0.315736,0.08(...TRUNCATED)
Scan (7).jpg
4,932
3,360
film_damage_simulator
[{"label":"dust","bbox":[0.209246,0.996429,0.210665,0.998512]},{"label":"dust","bbox":[0.810422,0.99(...TRUNCATED)
Scan (8).jpg
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3,384
film_damage_simulator
[{"label":"dust","bbox":[0.134102,0.663121,0.13572,0.665189]},{"label":"dust","bbox":[0.395833,0.661(...TRUNCATED)
Scan (9).jpg
4,944
3,384
film_damage_simulator
[{"label":"dust","bbox":[0.243123,0.043144,0.244943,0.045508]},{"label":"dust","bbox":[0.346076,0.66(...TRUNCATED)

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Project Halide Training Data

Film defect detection training data for MiniCPM-V 4.6 fine-tuning.

Dataset

FilmDamageSimulator (Eurographics 2023)

  • 10 film scans (4K resolution)
  • 12,137 defect annotations
  • 5 classes: dust, dirt, short_hair, long_hair, scratch
  • All bounding boxes normalized to [0.0-1.0]

Format

JSONL with structure:

Classes

Class Count Color
dust 7,631 Red
dirt 2,700 Orange
short_hair 1,341 Cyan
long_hair 399 Green
scratch 66 Yellow

Source

Original data from FilmDamageSimulator.

Citation:

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