Dataset Versions
Versions
2023-11-22 1:47pm
v27
· a year ago
2023-11-20 2:36pm
v26
· a year ago
2023-11-15 11:58pm
v25
· a year ago
2023-11-14 4:11pm
v24
· a year ago
2023-11-09 5:02pm
v23
· a year ago
2023-10-16 10:41am
v22
· a year ago
2023-10-16 10:22am
v21
· a year ago
2023-10-12 5:04pm
v20
· a year ago
2023-10-12 4:21pm
v19
· a year ago
2023-10-12 3:58pm
v18
· a year ago
2023-10-12 3:06pm
v17
· a year ago
2023-10-12 2:28pm
v16
· a year ago
2023-10-12 11:39am
v15
· a year ago
2023-10-12 11:04am
v14
· a year ago
2023-10-12 10:15am
v13
· a year ago
2023-10-11 4:35pm
v12
· a year ago
2023-10-11 10:59am
v11
· a year ago
2023-10-10 5:23pm
v10
· a year ago
2023-10-10 4:44pm
v9
· a year ago
2023-10-10 4:16pm
v8
· a year ago
2023-10-10 4:01pm
v7
· a year ago
2023-10-10 3:19pm
v6
· a year ago
2023-10-10 2:55pm
v5
· a year ago
2023-10-10 11:23am
v4
· a year ago
2023-10-09 4:32pm
v3
· a year ago
2023-10-09 4:14pm
v2
· a year ago
2023-10-09 3:35pm
v1
· a year ago
v23
2023-11-09 5:02pm
Generated on Nov 9, 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.
1857 Total Images
View All Images
Dataset Split
Train Set 98%
1827Images
Valid Set 2%
30Images
Test Set %
0Images
Preprocessing
Auto-Orient: Applied
Augmentations
Outputs per training example: 3
Grayscale: Apply to 25% of images
Blur: Up to 1px
Noise: Up to 2% of pixels