Dataset Versions
Versions
2022-09-05 9-05am - rift core - portal validation set
v16
· 2 years ago
2022-09-05 8-17am - rift core - fixed annotations
v15
· 2 years ago
2022-09-05 7-00am - rift core
v14
· 2 years ago
2022-09-05 5-04am - rift core bounding crop
v12
· 2 years ago
2022-06-26 6:34pm less blur more noise - gold
v11
· 2 years ago
2022-06-22 8:28pm 588x508 less noise more blur
v10
· 3 years ago
2022-06-22 7:11pm - 294x254 - no blur
v9
· 3 years ago
2022-06-22 7:07pm - 295x254 - no blur
v8
· 3 years ago
2022-06-22 3:30am - auto contrast - 0-40- crop
v7
· 3 years ago
2022-06-22 1:28am - less blur - more noise
v6
· 3 years ago
2022-06-21 9:41pm - mosaic
v5
· 3 years ago
2022-06-21 9:40pm - 25 cutouts smaller
v4
· 3 years ago
2022-06-21 9:38pm - 25 cutouts
v3
· 3 years ago
2022-06-21 9:36pm - bounding box noise-blur
v2
· 3 years ago
2022-06-21 9:34pm
v1
· 3 years ago
v7
2022-06-22 3:30am - auto contrast - 0-40- crop
Generated on Jun 22, 2022
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.
1046 Total Images
View All Images
Dataset Split
Train Set 88%
924Images
Valid Set 8%
81Images
Test Set 4%
41Images
Preprocessing
Auto-Orient: Applied
Resize: Stretch to 416x416
Auto-Adjust Contrast: Using Adaptive Equalization
Augmentations
Outputs per training example: 3
Crop: 0% Minimum Zoom, 40% Maximum Zoom
Noise: Up to 25% of pixels
Cutout: 25 boxes with 2% size each
Mosaic: Applied