SmartObject_colors Dataset
Versions
SmartObject_16cls_256x256_mosaic_whitepadded_noflip
v32
· 2 years ago
SmartObject_16cls_256x256_whitepadded_noflip
v31
· 2 years ago
SmartObject_16cls_256x256_whitepadded
v29
· 2 years ago
SmartObject_16cls_256x256_mosaic_whitepadded
v28
· 2 years ago
SmartObject_16cls_256x256_mosaic_whitepadded
v27
· 2 years ago
SmartObject_16cls_256x256_mosaic
v26
· 2 years ago
SmartObject_16cls_256x256_mosaic
v25
· 2 years ago
SmartObject_16cls_256x256_mosaic
v21
· 2 years ago
SmartObject_16cls_XxX_mosaic
v19
· 2 years ago
SmartObject_16cls_XxX_mosaic_tiled
v18
· 2 years ago
SmartObject_16cls_256x256_mosaic
v17
· 2 years ago
SmartObject_15cls_256x256_mosaic_tiled
v16
· 2 years ago
SmartObject_15cls_256x256_mosaic_null_tiled
v15
· 2 years ago
SmartObject_15cls_128x128
v11
· 2 years ago
SmartObject_15cls_256x256_padded
v10
· 2 years ago
SmartObject_15cls_256x256
v9
· 2 years ago
v15
SmartObject_15cls_256x256_mosaic_null_tiled
Generated on Jun 1, 2022
Popular Download Formats
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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599 Total Images
View All Images
Dataset Split
Train Set 100%
599Images
Valid Set %
0Images
Test Set %
0Images
Preprocessing
Auto-Orient: Applied
Resize: Stretch to 256x256
Tile: 2 rows x 2 columns
Filter Null: Require at least 90% of images to contain annotations.
Augmentations
Outputs per training example: 3
Flip: Horizontal, Vertical
Crop: 0% Minimum Zoom, 15% Maximum Zoom
Noise: Up to 1% of pixels
Mosaic: Applied