Dataset Versions
Versions
2025-01-08 5:10pm
v39
· 2 months ago
9 Ball Only 04 Jan 25
v38
· 2 months ago
2024-12-16 7:07pm
v36
· 2 months ago
2024-12-15 11:17pm
v35
· 2 months ago
2024-12-14 11:27pm
v34
· 2 months ago
2024-12-12 10:07pm
v31
· 2 months ago
2024-12-09 11:10pm
v29
· 3 months ago
2024-12-05 11:04pm
v27
· 3 months ago
2024-12-04 9:34pm
v25
· 3 months ago
2024-11-29 5:10pm
v16
· 3 months ago
2024-11-22 6:20pm
v13
· 3 months ago
2024-11-03 7:57am
v12
· 4 months ago
2024-11-02 9:19am
v11
· 4 months ago
2024-11-02 9:12am
v10
· 4 months ago
2024-11-02 8:46am
v9
· 4 months ago
2024-11-02 1:03am
v8
· 4 months ago
2024-11-01 11:29pm
v7
· 4 months ago
2024-11-01 12:05pm
v6
· 4 months ago
AramithBlackSet
v5
· 4 months ago
2024-10-21 6:24pm
v4
· 4 months ago
no augmentations
v3
· 4 months ago
2024-10-20 4:45am
v2
· 4 months ago
2024-10-11 1:05pm
v1
· 5 months ago
v12
2024-11-03 7:57am
Generated on Nov 3, 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.
2491 Total Images
View All ImagesDataset Split
Train Set 88%
2193Images
Valid Set 8%
211Images
Test Set 3%
87Images
Preprocessing
Auto-Orient: Applied
Resize: Fit within 640x640
Tile: 5 rows x 5 columns
Filter Null: Require at least 90% of images to contain annotations.
Augmentations
Outputs per training example: 3
Brightness: Between -20% and +20%
Blur: Up to 0.9px
Noise: Up to 0.73% of pixels