Dataset Versions
Versions
2023-03-03 8:04pm
v26
· 2 years ago
2023-02-23 12:09am
v25
· 2 years ago
YOLOV5s
v24
· 2 years ago
2023-02-21 4:10pm
v23
· 2 years ago
2023-02-20 8:22pm
v22
· 2 years ago
2023-02-19 10:10pm
v21
· 2 years ago
2023-02-19 9:07pm
v20
· 2 years ago
2023-02-19 7:41pm
v19
· 2 years ago
2023-02-16 7:05pm
v18
· 2 years ago
Insertaionnew
v17
· 2 years ago
EDITSIZE640x640
v16
· 2 years ago
2023-02-14 5:00pm
v15
· 2 years ago
UpdateRobot-5
v14
· 2 years ago
Robotyolov5
v13
· 2 years ago
2023-02-09 11:01pm
v12
· 2 years ago
2023-02-09 11:01pm
v11
· 2 years ago
RobotFHKIEL3
v10
· 2 years ago
2023-02-09 10:13pm
v9
· 2 years ago
Robotfhkiel2
v8
· 2 years ago
2023-02-08 3:37pm
v7
· 2 years ago
2023-02-08 3:35pm
v6
· 2 years ago
RobotFHkiel1
v5
· 2 years ago
2023-02-08 1:13am
v4
· 2 years ago
2023-02-08 1:12am
v3
· 2 years ago
2023-02-08 1:11am
v2
· 2 years ago
2023-02-07 9:16pm
v1
· 2 years ago
v11
2023-02-09 11:01pm
Generated on Feb 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.
451 Total Images
View All Images
Dataset Split
Train Set 91%
411Images
Valid Set 5%
21Images
Test Set 4%
19Images
Preprocessing
Grayscale: Applied
Augmentations
Outputs per training example: 3
Flip: Horizontal
Saturation: Between -37% and +37%
Brightness: Between -25% and +25%
Blur: Up to 3.25px
Noise: Up to 5% of pixels
Cutout: 12 boxes with 5% size each
Bounding Box: Brightness: Between -27% and +27%