Dataset Versions
Versions
-v24part3 for training-
v31
· a year ago
-v24part2 for training-
v30
· a year ago
-v24part1 for training-
v29
· a year ago
-v23part2 for training-
v28
· a year ago
-v23part1 for training-
v27
· a year ago
2023-11-15 11:18am
v26
· a year ago
2023-11-15 10:40am
v25
· a year ago
2023-11-01 4:50pm
v23
· a year ago
2023-10-26 4:30pm
v22
· a year ago
2023-10-26 1:10pm
v21
· a year ago
2023-10-26 12:37pm
v20
· a year ago
2023-10-26 12:22pm
v19
· a year ago
2023-10-24 11:49am
v17
· a year ago
2023-10-19 4:59pm
v15
· a year ago
2023-10-18 6:30pm
v14
· a year ago
2023-10-18 6:07pm
v13
· a year ago
2023-10-18 6:01pm
v12
· a year ago
2023-10-10 5:17pm
v11
· a year ago
2023-10-04 6:53pm
v10
· a year ago
2023-10-01 12:39pm
v9
· a year ago
2023-10-01 11:20am
v8
· a year ago
2023-09-28 3:25pm
v7
· a year ago
2023-09-28 3:04pm
v6
· a year ago
2023-09-26 1:39pm
v5
· a year ago
v26
2023-11-15 11:18am
Generated on Nov 15, 2023
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.
57 Total Images
View All ImagesDataset Split
Train Set 89%
51Images
Valid Set 7%
4Images
Test Set 4%
2Images
Preprocessing
Auto-Orient: Applied
Modify Classes: 6 remapped, 2 dropped
Augmentations
Outputs per training example: 3
Flip: Horizontal
Rotation: Between -15° and +15°
Noise: Up to 5% of pixels