Dataset Versions
Versions
2024-01-19 2:43am
v37
· a year ago
2024-01-19 2:28am
v36
· a year ago
2024-01-19 2:21am
v35
· a year ago
2023-09-10 9:17pm
v34
· a year ago
2022-12-20 12:11am
v33
· 2 years ago
2022-12-20 12:10am
v32
· 2 years ago
2022-12-19 11:49pm
v31
· 2 years ago
Final Dataset
v30
· 2 years ago
2022-12-05 1:25pm
v29
· 2 years ago
2022-12-04 5:44pm
v28
· 2 years ago
2022-10-05 10:01pm
v27
· 2 years ago
2022-10-05 9:42pm
v26
· 2 years ago
2022-10-05 9:37pm
v25
· 2 years ago
2022-10-05 9:05pm
v19
· 2 years ago
2022-10-04 11:22pm
v18
· 2 years ago
2022-10-02 3:09pm
v17
· 2 years ago
2022-10-02 2:42pm
v16
· 2 years ago
2022-10-01 2:45am
v15
· 2 years ago
2022-10-01 2:37am
v14
· 2 years ago
2022-10-01 2:32am
v13
· 2 years ago
2022-10-01 2:30am
v12
· 2 years ago
2022-10-01 2:28am
v11
· 2 years ago
2022-10-01 1:44am
v10
· 2 years ago
2022-09-30 8:09pm
v7
· 2 years ago
2022-09-30 8:02pm
v6
· 2 years ago
2022-09-30 2:17pm
v5
· 2 years ago
2022-09-29 10:47pm
v4
· 2 years ago
2022-09-29 4:56pm
v3
· 2 years ago
2022-09-29 4:28pm
v2
· 2 years ago
2022-09-29 12:29am
v1
· 2 years ago
v25
2022-10-05 9:37pm
Generated on Oct 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.
Other Formats
Choose another format.
5574 Total Images
View All Images
Dataset Split
Train Set 90%
5004Images
Valid Set 9%
504Images
Test Set 1%
66Images
Preprocessing
Auto-Orient: Applied
Resize: Fit within 640x640
Auto-Adjust Contrast: Using Histogram Equalization
Tile: 2 rows x 1 columns
Modify Classes: 1 remapped, 0 dropped
Filter Null: Do not filter any null images.
Augmentations
Outputs per training example: 3
Flip: Horizontal, Vertical
90° Rotate: Clockwise, Counter-Clockwise, Upside Down
Rotation: Between -12° and +12°
Shear: ±11° Horizontal, ±11° Vertical
Brightness: Between -13% and +13%
Exposure: Between -4% and +4%
Blur: Up to 3.5px
Noise: Up to 8% of pixels
Cutout: 5 boxes with 2% size each
Bounding Box: Exposure: Between -13% and +13%