Dataset Versions
Versions
kytu-Bien3-addBien-zin
v23
· 3 years ago
bien3-kytu-zin
v22
· 3 years ago
bien3-31char-zin
v21
· 3 years ago
2022-05-19 4:15pm-train
v20
· 3 years ago
2022-05-19 4:11pm-ori
v19
· 3 years ago
chuanalpha-2022-05-17 3:41pm
v18
· 3 years ago
chuan-2022-05-17 2:47pm
v17
· 3 years ago
chuanhoatt-2022-05-17 2:14pm
v16
· 3 years ago
2022-05-16 6:18pm
v15
· 3 years ago
2022-05-16 11:41am
v14
· 3 years ago
2022-05-13 8:55am
v13
· 3 years ago
2022-05-12 5:02pm
v12
· 3 years ago
2022-05-12 5:01pm
v11
· 3 years ago
2022-05-10 6:20pm
v10
· 3 years ago
2022-05-10 6:15pm
v9
· 3 years ago
2022-05-10 6:12pm
v8
· 3 years ago
2022-05-10 6:08pm
v7
· 3 years ago
2022-05-06 2:02pm
v6
· 3 years ago
2022-05-06 1:56pm
v5
· 3 years ago
2022-05-05 5:23pm
v4
· 3 years ago
2022-05-05 3:53pm
v3
· 3 years ago
2022-05-05 3:38pm
v2
· 3 years ago
2022-04-25 5:52pm
v1
· 3 years ago
v4
2022-05-05 5:23pm
Generated on May 5, 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.
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191 Total Images
View All Images
Dataset Split
Train Set 90%
171Images
Valid Set %
0Images
Test Set 10%
20Images
Preprocessing
Resize: Stretch to 416x416
Auto-Adjust Contrast: Using Histogram Equalization
Grayscale: Applied
Augmentations
Outputs per training example: 3
Shear: ±15° Horizontal, ±15° Vertical
Noise: Up to 15% of pixels
Mosaic: Applied