Dataset Versions
Versions
character31
v45
· 3 years ago
character30
v44
· 3 years ago
character29
v43
· 3 years ago
character28
v42
· 3 years ago
character27
v41
· 3 years ago
character26
v40
· 3 years ago
character25
v39
· 3 years ago
character24
v38
· 3 years ago
character23
v37
· 3 years ago
character22
v36
· 3 years ago
character21
v35
· 3 years ago
character_test2
v34
· 3 years ago
character_test
v33
· 3 years ago
character20
v32
· 3 years ago
character19
v31
· 3 years ago
character18
v30
· 3 years ago
character17
v29
· 3 years ago
character16
v28
· 3 years ago
character15
v27
· 3 years ago
character14
v26
· 3 years ago
character13
v25
· 3 years ago
character12
v24
· 3 years ago
character11
v23
· 3 years ago
character_test
v22
· 3 years ago
character10
v21
· 3 years ago
character09
v20
· 3 years ago
character08
v19
· 3 years ago
character07
v18
· 3 years ago
character06
v17
· 3 years ago
character05
v16
· 3 years ago
character04
v15
· 3 years ago
character03
v14
· 3 years ago
character02
v13
· 3 years ago
character01
v12
· 3 years ago
v16
character05
Generated on Apr 19, 2022
Popular Download Formats
YOLOv11
TXT annotations and YAML config used with YOLOv11.
YOLOv9
TXT annotations and YAML config used with YOLOv9.
YOLOv8
TXT annotations and YAML config used with YOLOv8.
YOLOv5
TXT annotations and YAML config used with YOLOv5.
YOLOv7
TXT annotations and YAML config used with YOLOv7.
COCO JSON
COCO JSON annotations are used with EfficientDet Pytorch and Detectron 2.
YOLO Darknet
Darknet TXT annotations used with YOLO Darknet (both v3 and v4) and YOLOv3 PyTorch.
Pascal VOC XML
Common XML annotation format for local data munging (pioneered by ImageNet).
TFRecord
TFRecord binary format used for both Tensorflow 1.5 and Tensorflow 2.0 Object Detection models.
PaliGemma
PaliGemma JSONL format used for fine-tuning PaliGemma, Google's open multimodal vision model.
CreateML JSON
CreateML JSON format is used with Apple's CreateML and Turi Create tools.
Other Formats
Choose another format.
477 Total Images
View All Images
Dataset Split
Train Set 100%
477Images
Valid Set %
0Images
Test Set %
0Images
Preprocessing
Resize: Stretch to 416x416
Augmentations
Outputs per training example: 3
Rotation: Between -5° and +5°
Shear: ±6° Horizontal, ±6° Vertical
Grayscale: Apply to 25% of images
Brightness: Between -5% and +5%
Blur: Up to 0.25px
Noise: Up to 1% of pixels