Dataset Versions
Versions
2022-08-30 8:35pm
v41
· 2 years ago
2022-08-30 7:33pm
v40
· 2 years ago
Successful Augmentations
v39
· 2 years ago
Re-Mapping Successful
v38
· 2 years ago
No Pre-Procs- Successful Generation
v37
· 2 years ago
2022-08-30 11:02am
v36
· 2 years ago
2022-08-30 12:18pm
v35
· 2 years ago
2022-08-25 8:44pm
v34
· 2 years ago
2022-08-25 6:02pm
v33
· 2 years ago
2022-08-23 6:28pm
v32
· 2 years ago
2022-08-23 1:39pm
v31
· 2 years ago
2022-08-23 12:47pm
v30
· 2 years ago
2022-08-23 12:39pm
v29
· 2 years ago
2022-08-23 10:07am
v28
· 2 years ago
2022-08-20 12:18am
v26
· 2 years ago
2022-08-20 12:18am
v25
· 2 years ago
2022-08-19 6:57pm
v24
· 2 years ago
2022-08-19 8:09am
v23
· 2 years ago
2022-08-19 11:32am
v22
· 2 years ago
2022-08-19 1:42am
v21
· 2 years ago
2022-08-19 1:05am
v20
· 2 years ago
2022-08-18 11:56pm
v18
· 2 years ago
2022-08-18 11:02pm
v17
· 2 years ago
2022-08-18 10:05pm
v16
· 2 years ago
2022-08-18 9:25pm
v14
· 2 years ago
2022-08-14 11:59am
v3
· 2 years ago
2022-07-19 10:18am
v1
· 2 years ago
v40
2022-08-30 7:33pm
Generated on Aug 30, 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.
4572 Total Images
View All Images
Dataset Split
Train Set 80%
3651Images
Valid Set 14%
661Images
Test Set 6%
260Images
Preprocessing
Isolate Objects: Applied
Static Crop: 7-100% Horizontal Region, 3-100% Vertical Region
Auto-Adjust Contrast: Using Adaptive Equalization
Modify Classes: 0 remapped, 2 dropped
Augmentations
Outputs per training example: 3
Flip: Horizontal
90° Rotate: Clockwise, Counter-Clockwise
Grayscale: Apply to 25% of images
Exposure: Between -6% and +6%
Noise: Up to 5% of pixels