Dataset Versions
Versions
2024-11-13 12:07pm
v29
· 15 days ago
2024-11-13 10:41am
v28
· 15 days ago
2024-11-13 10:16am
v27
· 15 days ago
2024-11-10 7:34am
v26
· 18 days ago
2024-11-07 1:11pm
v25
· 21 days ago
2024-10-10 7:59am
v24
· 2 months ago
2024-10-07 8:36am
v23
· 2 months ago
2023-08-03 3:03pm
v22
· a year ago
2023-08-03 12:36pm
v21
· a year ago
2023-08-01 3:09pm
v20
· a year ago
2023-08-01 2:46pm
v19
· a year ago
2023-08-01 2:05pm
v18
· a year ago
2023-08-01 1:01pm
v17
· a year ago
2023-08-01 12:19pm
v16
· a year ago
2023-07-30 2:10pm
v15
· a year ago
2023-07-30 12:54pm
v14
· a year ago
2023-07-30 12:35pm
v13
· a year ago
2023-07-30 11:48am
v12
· a year ago
2023-02-27 1:51pm
v11
· 2 years ago
2023-02-27 1:22pm
v10
· 2 years ago
2023-02-27 10:08am
v6
· 2 years ago
2023-02-27 8:50am
v5
· 2 years ago
2023-02-23 2:55pm
v4
· 2 years ago
2023-02-23 2:51pm
v3
· 2 years ago
2023-02-23 2:24pm
v2
· 2 years ago
2023-02-23 2:22pm
v1
· 2 years ago
v14
2023-07-30 12:54pm
Generated on Jul 30, 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.
220 Total Images
View All Images
Dataset Split
Train Set 87%
192Images
Valid Set 9%
19Images
Test Set 4%
9Images
Preprocessing
Auto-Adjust Contrast: Using Adaptive Equalization
Augmentations
Outputs per training example: 3
Hue: Between -25° and +25°
Brightness: Between -25% and +25%
Exposure: Between -25% and +25%
Blur: Up to 1.5px
Noise: Up to 5% of pixels