Dataset Versions
Versions
2024-11-15 2:53pm
v30
· a month ago
2024-10-16 1:14pm
v27
· 2 months ago
2024-09-29 12:00am
v26
· 3 months ago
2024-09-15 1:12pm
v25
· 3 months ago
2024-06-24 9:05am
v24
· 6 months ago
2024-04-28 12:41pm
v23
· 8 months ago
2024-04-21 10:27pm
v22
· 8 months ago
2024-04-07 2:59pm
v21
· 8 months ago
2024-04-07 8:01am
v20
· 8 months ago
2024-04-05 9:25pm
v19
· 8 months ago
80-
v18
· a year ago
No augments
v17
· a year ago
2024-01-07 8:38pm
v16
· a year ago
2024-01-07 1:12am
v15
· a year ago
2024-01-07 12:08am
v14
· a year ago
2023-12-31 3:57pm
v13
· a year ago
2023-12-03 10:46pm
v12
· a year ago
yolov8 no augment updated dataset
v11
· a year ago
752 Images -70-20-10- no augment
v10
· a year ago
2023-12-03 2:30pm
v9
· a year ago
yolo v5 no augment
v8
· a year ago
Rodriguez-test
v7
· a year ago
yolov8m w augment
v6
· a year ago
retinanet keras no augment
v5
· a year ago
yolov8m no augment
v4
· a year ago
2023-11-30 11:31pm
v3
· a year ago
2023-11-30 10:49pm
v1
· a year ago
v14
2024-01-07 12:08am
Generated on Jan 6, 2024
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.
2325 Total Images
View All Images
Dataset Split
Train Set 88%
2035Images
Valid Set 8%
192Images
Test Set 4%
98Images
Preprocessing
Auto-Orient: Applied
Static Crop: 25-75% Horizontal Region, 25-75% Vertical Region
Augmentations
Outputs per training example: 3
Exposure: Between -10% and +10%
Bounding Box: Exposure: Between -25% and +25%