Dataset Versions
Versions
2024-03-14 2:32pm
v14
· 8 months ago
2023-05-23 7:28am
v12
· a year ago
YOLOv8n-100epochs
v11
· 2 years ago
v5_2-resized640_aug7x-TrainedFromv6Chkpt
v10
· 2 years ago
v5_1-resized640_aug7x-TrainedFromv8Chkpt
v9
· 2 years ago
v3-resized640_aug7x
v8
· 2 years ago
v2-resized640_aug7x
v7
· 2 years ago
resized640_aug7x
v6
· 2 years ago
resized640_aug10x-FirstUploadSet-ForLabelAssist-ACCURATE
v4
· 2 years ago
resized640_aug10x-FirstUploadSet-ForLabelAssist-FAST
v3
· 2 years ago
IsolateObjects_FirstUploadSet
v2
· 2 years ago
raw-images_FirstUploadSet
v1
· 2 years ago
v10
v5_2-resized640_aug7x-TrainedFromv6Chkpt
Generated on Mar 13, 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.
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15874 Total Images
View All Images
Dataset Split
Train Set 94%
14966Images
Valid Set 4%
588Images
Test Set 2%
320Images
Preprocessing
Auto-Orient: Applied
Resize: Stretch to 640x640
Augmentations
Outputs per training example: 7
Flip: Horizontal
Rotation: Between -12° and +12°
Shear: ±2° Horizontal, ±2° Vertical
Grayscale: Apply to 10% of images
Hue: Between -25° and +25°
Saturation: Between -25% and +25%
Brightness: Between -25% and +25%
Exposure: Between -25% and +25%
Blur: Up to 1.5px
Noise: Up to 1% of pixels
Cutout: 7 boxes with 3% size each