Dataset Versions
Versions
2022-01-27 10:29am
v29
· 3 years ago
2022-01-27 10:28am
v28
· 3 years ago
2022-01-27 10:27am
v27
· 3 years ago
2022-01-27 10:27am
v26
· 3 years ago
2022-01-27 10:27am
v25
· 3 years ago
2022-01-27 10:26am
v24
· 3 years ago
2022-01-27 10:17am
v23
· 3 years ago
2022-01-27 10:17am
v22
· 3 years ago
2022-01-27 10:16am
v21
· 3 years ago
2022-01-27 10:16am
v20
· 3 years ago
2022-01-27 10:15am
v19
· 3 years ago
2022-01-27 10:15am
v18
· 3 years ago
2022-01-27 10:15am
v17
· 3 years ago
2022-01-27 10:15am
v16
· 3 years ago
2022-01-27 10:14am
v15
· 3 years ago
2022-01-27 10:05am
v14
· 3 years ago
2022-01-27 9:58am
v13
· 3 years ago
2022-01-27 9:58am
v12
· 3 years ago
2022-01-27 9:58am
v11
· 3 years ago
2022-01-27 9:57am
v10
· 3 years ago
2022-01-27 9:57am
v9
· 3 years ago
2022-01-27 9:56am
v8
· 3 years ago
2022-01-26 7:27pm
v7
· 3 years ago
2022-01-26 7:26pm
v6
· 3 years ago
2022-01-26 7:26pm
v5
· 3 years ago
2022-01-26 7:26pm
v4
· 3 years ago
2022-01-26 7:25pm
v3
· 3 years ago
2022-01-26 7:25pm
v2
· 3 years ago
2022-01-26 7:24pm
v1
· 3 years ago
v22
2022-01-27 10:17am
Generated on Jan 27, 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.
43 Total Images
View All Images
Dataset Split
Train Set 84%
36Images
Valid Set 9%
4Images
Test Set 7%
3Images
Preprocessing
Resize: Stretch to 640x640
Grayscale: Applied
Auto-Adjust Contrast: Using Histogram Equalization
Augmentations
Outputs per training example: 2
Saturation: Between -25% and +25%