Dataset Versions
Versions
2023-03-31 6:05pm
v47
· 2 years ago
2023-03-07 10:40am
v46
· 2 years ago
2023-03-07 10:38am
v45
· 2 years ago
2023-03-07 9:40am
v44
· 2 years ago
Noise_2X
v43
· 2 years ago
2023-02-18 1:14pm
v42
· 2 years ago
Blur_2X
v41
· 2 years ago
Exposure_2X
v40
· 2 years ago
Brightness_2X
v39
· 2 years ago
Saturation_2X
v38
· 2 years ago
Grayscale_2X
v37
· 2 years ago
Crop_2X
v36
· 2 years ago
90- Rotate_2X
v35
· 2 years ago
Flip_2X
v34
· 2 years ago
Noise_1X
v33
· 2 years ago
Blur_1X
v32
· 2 years ago
Exposure_1X
v31
· 2 years ago
Brightness_1X
v30
· 2 years ago
Saturation_1X
v29
· 2 years ago
Grayscale_1X
v28
· 2 years ago
Crop_1X
v26
· 2 years ago
90-Rotate_1X
v25
· 2 years ago
Flip_1X
v24
· 2 years ago
blur_3p
v23
· 2 years ago
Saturation
v22
· 2 years ago
original
v21
· 2 years ago
Grayscale
v20
· 2 years ago
Noise
v19
· 2 years ago
Blur
v18
· 2 years ago
Exposure
v17
· 2 years ago
Brightness
v16
· 2 years ago
2023-02-17 7:31pm
v15
· 2 years ago
Crop
v14
· 2 years ago
90- Rotate
v13
· 2 years ago
Flip-Horizontal- Vertical
v12
· 2 years ago
v47
2023-03-31 6:05pm
Generated on Mar 31, 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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722 Total Images
View All ImagesDataset Split
Train Set 92%
667Images
Valid Set 8%
55Images
Test Set %
0Images
Preprocessing
Auto-Orient: Applied
Augmentations
Outputs per training example: 3
Brightness: Between -50% and +50%