Dataset Versions
Versions
2024-11-07 6:42pm
v36
· 2 months ago
2024-11-07 6:41pm
v35
· 2 months ago
2024-10-28 2:09pm
v34
· 2 months ago
2024-10-28 2:08pm
v33
· 2 months ago
2024-10-28 2:07pm
v32
· 2 months ago
2024-10-26 8:18am
v30
· 2 months ago
2024-10-24 10:32am
v29
· 2 months ago
2024-10-24 9:05am
v28
· 2 months ago
cropped_resized
v27
· 2 months ago
2024-10-21 7:15pm
v23
· 2 months ago
only_ROI
v22
· 2 months ago
2024-10-21 11:13am
v21
· 2 months ago
2024-10-21 10:31am
v19
· 2 months ago
2024-10-21 9:36am
v16
· 2 months ago
2024-10-21 6:16am
v14
· 2 months ago
2024-10-16 11:39am
v13
· 2 months ago
2024-10-16 8:32am
v11
· 2 months ago
2024-10-16 8:29am
v10
· 2 months ago
2024-10-16 8:06am
v9
· 2 months ago
2024-10-10 3:52pm
v8
· 3 months ago
2024-10-07 6:01pm
v7
· 3 months ago
2024-10-07 5:42pm
v6
· 3 months ago
2024-10-07 5:31pm
v5
· 3 months ago
2024-10-06 12:25pm
v4
· 3 months ago
2024-10-06 9:57am
v3
· 3 months ago
2024-10-03 7:55pm
v2
· 3 months ago
2024-10-03 3:45pm
v1
· 3 months ago
v11
2024-10-16 8:32am
Generated on Oct 16, 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.
201 Total Images
View All Images
Dataset Split
Train Set 88%
177Images
Valid Set 6%
12Images
Test Set 6%
12Images
Preprocessing
Auto-Orient: Applied
Resize: Fill (with center crop) in 640x640
Grayscale: Applied
Filter Null: Require at least 30% of images to contain annotations.
Augmentations
Outputs per training example: 3
90° Rotate: Clockwise, Counter-Clockwise, Upside Down
Noise: Up to 0.5% of pixels