Dataset Versions
Versions
2024-12-18 2:58pm
v26
· 11 days ago
2024-12-18 2:57pm
v25
· 11 days ago
2024-12-10 10:29am
v24
· 19 days ago
2024-12-04 10:52pm
v23
· 25 days ago
2024-12-04 10:51pm
v22
· 25 days ago
2024-12-03 5:23pm
v21
· a month ago
2024-12-03 12:31pm
v20
· a month ago
2024-11-30 4:00pm
v19
· a month ago
2024-11-30 3:58pm
v18
· a month ago
2024-11-30 3:56pm
v17
· a month ago
2024-11-29 3:53pm
v16
· a month ago
2024-11-29 12:41pm
v15
· a month ago
2024-11-29 12:40pm
v14
· a month ago
2024-11-29 12:38pm
v13
· a month ago
2024-11-29 12:38pm
v12
· a month ago
2024-11-29 12:28pm
v11
· a month ago
dzadza
v10
· a month ago
2024-11-22 9:24am
v7
· a month ago
2024-11-22 9:23am
v6
· a month ago
2024-11-22 9:23am
v5
· a month ago
2024-11-20 6:25pm
v4
· a month ago
2024-11-20 6:21pm
v3
· a month ago
2024-11-20 6:20pm
v2
· a month ago
2024-09-11 8:48am
v1
· 4 months ago
v14
2024-11-29 12:40pm
Generated on Nov 29, 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.
7 Total Images
View All Images
Dataset Split
Train Set 43%
3Images
Valid Set 43%
3Images
Test Set 14%
1Images
Preprocessing
Isolate Objects: Applied
Dynamic Crop: Class: cars
Grayscale: Applied
Auto-Adjust Contrast: Using Adaptive Equalization
Augmentations
Outputs per training example: 3
Flip: Horizontal
Crop: 0% Minimum Zoom, 20% Maximum Zoom
Shear: ±10° Horizontal, ±10° Vertical
Hue: Between -15° and +15°
Brightness: Between -15% and +15%
Noise: Up to 0.1% of pixels