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Datasets:
UmaDiffusion
/
ULTIMA-YOLO

Tasks:
Object Detection
Languages:
English
Size Categories:
1K<n<10K
License:
Dataset card Files Community
Dataset Viewer
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The dataset viewer is not available for this dataset.
Cannot get the config names for the dataset.
Error code:   ConfigNamesError
Exception:    ValueError
Message:      Couldn't infer the same data file format for all splits. Got {NamedSplit('train'): ('imagefolder', {}), NamedSplit('validation'): ('imagefolder', {}), NamedSplit('test'): ('text', {})}
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response
                  config_names = get_dataset_config_names(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 347, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1879, in dataset_module_factory
                  raise e1 from None
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1854, in dataset_module_factory
                  return HubDatasetModuleFactoryWithoutScript(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1245, in get_module
                  module_name, default_builder_kwargs = infer_module_for_data_files(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 593, in infer_module_for_data_files
                  raise ValueError(f"Couldn't infer the same data file format for all splits. Got {split_modules}")
              ValueError: Couldn't infer the same data file format for all splits. Got {NamedSplit('train'): ('imagefolder', {}), NamedSplit('validation'): ('imagefolder', {}), NamedSplit('test'): ('text', {})}

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About ULTIMA-YOLO

This is a repository for subproject of ULTIMA.

ULTIMA-YOLO contains yolo-format bounding box of Uma Musume and corresponding image pair.

label_data.txt
[object_id] [x_center] [y_center] [width] [height]
[object_id] [x_center] [y_center] [width] [height]
...

132 characters in Uma Musume are in ULTIMA-YOLO dataset.

Trained Model, based on yolov9-e, is in ULTIMA-YOLOv9

Statistics

  • Train: 3,991 images
  • Val: 399 images
  • All labels have been manually created through human effort, with workers ethically compensated with "passion pay", a term came from south korea.
  • Each image is named to indicate its source.
Character Name # in Train # in Val Precision Recall mAP50 mAP50-95
Agnes Tachyon 187 35 0.957 0.886 0.961 0.765
Air Groove 87 12 1 0.835 0.933 0.713
Air Shakur 75 12 0.986 1 0.995 0.909
Akikawa Yayoi 25 3 1 0.693 0.995 0.648
Admire Vega 74 16 1 0.754 0.894 0.707
Agnes Digital 50 6 0.992 0.833 0.972 0.803
Anshinzawa Sasami 25 1 0.956 1 0.995 0.796
Aston Machan 55 3 1 0.726 0.995 0.912
Bamboo Memory 41 3 0.97 1 0.995 0.895
Biko Pegasus 34 3 0.972 1 0.995 0.84
Byerley Turk 43 2 0.951 1 0.995 0.855
Bitter Glace 24 0 0.888 0.875 0.944 0.776
Biwa Hayahide 52 8 0.821 1 0.995 0.846
Copano Rickey 51 5 0.969 0.667 0.864 0.69
Curren Chan 54 9 0.996 1 0.995 0.801
Cheval Grand 43 13 0.998 1 0.995 0.783
Twin Turbo 120 13 0.982 1 0.995 0.842
Daiichi Ruby 57 5 0.963 1 0.995 0.949
Darley Arabian 48 2 1 0.837 0.995 0.819
Daring Tact 62 5 0.997 1 0.995 0.841
Daitaku Helios 100 11 1 0.903 0.961 0.787
Daiwa Scarlet 114 19 0.987 1 0.995 0.707
El Condor Pasa 65 6 0.951 1 0.995 0.808
Eishin Flash 39 5 0.853 1 0.995 0.927
Fuji Kiseki 48 6 1 0.875 0.995 0.88
Fine Motion 55 7 0.989 0.875 0.906 0.71
Gold City 49 8 0.942 0.938 0.991 0.81
Gold Ship 146 16 0.858 1 0.995 0.895
Godolphin Barb 44 2 0.84 0.833 0.851 0.659
Grass Wonder 74 6 1 0.797 0.995 0.792
Hishi Akebono 39 4 0.989 1 0.995 0.766
Hishi Amazon 46 6 0.993 1 0.995 0.835
Hayakawa Tazuna 34 5 1 0.659 0.922 0.638
Hishi Miracle 52 6 0.971 0.75 0.945 0.769
Happy Meek 51 4 1 0.787 0.938 0.808
Hokko Tarumae 50 9 1 0.678 0.995 0.76
Haru Urara 69 9 0.986 0.917 0.989 0.747
Ikuno Dictus 96 12 0.873 1 0.995 0.858
Ines Fujin 41 7 0.947 1 0.995 0.898
Inari One 46 2 0.856 1 0.995 0.656
Jungle Pocket 53 6 1 0.85 0.995 0.747
King Halo 77 6 0.975 1 0.995 0.773
Kashimoto Riko 34 3 1 0.778 0.995 0.823
Kiryuin Aoi 44 4 0.997 0.895 0.929 0.712
Kitasan Black 116 19 0.974 1 0.995 0.909
K.S.Miracle 48 3 0.982 1 0.995 0.852
Katsuragi Ace 43 4 0.989 1 0.995 0.881
Kawakami Princess 50 7 0.975 1 0.995 0.841
Little Cocon 51 3 1 0.567 0.995 0.796
Light Hello 25 2 0.993 1 0.995 0.788
Mr. C.B. 91 13 1 0.659 0.995 0.703
Meisho Doto 59 7 0.988 1 0.995 0.782
Mihono Bourbon 84 13 1 0.955 0.994 0.779
Manhattan Cafe 144 32 0.876 0.884 0.967 0.797
Mejiro Ardan 58 8 0.985 0.833 0.869 0.723
Mejiro Bright 55 6 0.987 1 0.995 0.813
Mejiro Dober 56 5 0.981 0.933 0.972 0.785
Mejiro McQueen 272 30 0.98 1 0.995 0.873
Mejiro Ryan 43 7 0.998 1 0.995 0.849
Matikanefukukitaru 52 7 1 0.952 0.995 0.719
Matikanetannhauser 87 13 0.996 1 0.995 0.81
Mejiro Palmer 95 11 0.893 1 0.929 0.822
Mejiro Ramonu 52 9 0.993 1 0.995 0.748
Maruzensky 43 7 0.984 1 0.995 0.684
Marvelous Sunday 40 6 1 0.702 0.995 0.668
Nakayama Festa 47 7 0.992 1 0.995 0.829
Nice Nature 96 8 0.993 1 0.995 0.723
Narita Brian 86 13 0.827 1 0.962 0.778
Narita Taishin 55 5 0.899 0.857 0.978 0.938
Nishino Flower 48 7 0.97 1 0.995 0.72
Narita Top Road 50 9 0.988 1 0.995 0.834
Oguri Cap 94 10 0.997 0.92 0.945 0.744
Rice Shower 165 25 0.992 1 0.995 0.89
Sakura Bakushin O 55 7 1 0.949 0.995 0.795
Symboli Rudolf 157 17 0.987 0.889 0.975 0.748
Sakura Chiyono O 48 9 0.946 0.8 0.941 0.835
Seiun Sky 72 10 0.98 1 0.995 0.842
Sakura Laurel 44 6 0.944 1 0.995 0.895
Shinko Windy 46 1 0.96 1 0.995 0.949
Seeking the Pearl 34 2 0.985 1 0.995 0.844
Symboli Kris S 68 6 0.87 0.958 0.943 0.728
Smart Falcon 53 7 0.976 1 0.995 0.876
Super Creek 48 4 1 0.959 0.995 0.736
Special Week 147 14 1 0.975 0.995 0.777
Silence Suzuka 129 18 0.993 1 0.995 0.84
Sirius Symboli 60 9 0.962 1 0.995 0.849
Satono Crown 47 2 0.993 0.75 0.925 0.746
Satono Diamond 79 12 0.98 0.75 0.775 0.649
Sweep Tosho 42 4 0.951 1 0.995 0.895
Tap Dance City 49 4 0.995 1 0.995 0.832
Taiki Shuttle 50 7 0.883 1 0.939 0.756
Tokai Teio 239 23 0.994 1 0.995 0.56
Tamamo Cross 59 6 1 0.86 0.99 0.748
T.M. Opera O 85 13 0.986 1 0.995 0.838
Tanino Gimlet 52 6 0.986 1 0.995 0.771
Mayano Top Gun 70 5 1 0.824 0.995 0.787
Tosen Jordan 68 9 0.959 1 0.995 0.801
Tsurumaru Tsuyoshi 38 2 0.984 1 0.995 0.736
Neo Universe 47 5 1 0.806 0.945 0.753
Vodka 110 15 0.954 1 0.995 0.895
Wonder Acute 53 1 0.976 0.8 0.962 0.877
Winning Ticket 47 5 0.997 1 0.995 0.889
Yukino Bijin 44 7 1 0.965 0.995 0.904
Yaeno Muteki 39 5 0.975 1 0.995 0.932
Yamanin Zephyr 42 3 0.976 0.714 0.96 0.747
Zenno Rob Roy 51 7 0.958 1 0.995 0.895
Furioso 15 0 0.938 1 0.995 0.995
Transcend 40 2 0.964 1 0.995 0.796
Espoir City 30 1 0.939 1 0.995 0.895
North Flight 40 2 0.946 1 0.995 0.597
Dantsu Flame 30 1 0.878 1 0.995 0.895
No Reason 26 0 0.961 0.667 0.699 0.53
Still in Love 28 1 0.961 1 0.995 0.895
Samson Big 25 1 0.891 1 0.995 0.697
Sounds of Earth 53 3 0.972 1 0.995 0.857
Royce and Royce 30 2 0.942 1 0.995 0.398
Duramente 43 1 0.939 1 0.995 0.895
Rhein Kraft 31 3 0.975 1 0.995 0.799
Cesario 37 1 0.947 1 0.995 0.796
Air Messiah 23 1 0.964 1 0.995 0.927
Daring Heart 28 0 0.961 1 0.995 0.858
Orfevre 25 3 0.947 1 0.995 0.995
Gentildonna 40 1 0.944 1 0.995 0.597
Win Variation 21 2 0.94 1 0.995 0.895
Venus Paques 37 2 0.935 1 0.995 0.796
Rigantona 28 1 0.995 1 0.995 0.91
Sonon Elfie 29 1 0.994 1 0.995 0.815
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Models trained or fine-tuned on UmaDiffusion/ULTIMA-YOLO