see-sci Image Dataset
Versions
2024-04-25 2-09pm - for pre-labelling model -no resize-
v35
Apr 25, 2024
2024-04-24 9-50am - for pre-labelling model
v34
Apr 24, 2024
2023-04-25 11:20am
v33
Apr 25, 2023
3x3 tiles
v32
Jan 6, 2023
4x5 tiles
v31
Jan 6, 2023
Full images
v30
Jan 6, 2023
4x5 tiled images 2023-01-04 12-58pm
v29
Jan 4, 2023
3x3 tiled images 2023-01-04 12-56pm
v28
Jan 4, 2023
Full images 2023-01-04 12-54pm
v27
Jan 4, 2023
rotifer_parts
v26
Jul 6, 2022
RF-Support-Test_tiling
v25
Jul 6, 2022
rotifer_parts_for_keypoint2
v24
Jul 1, 2022
rotifer_parts_for_keypoint
v23
Jul 1, 2022
no_tiling
v22
Jun 27, 2022
beadClusterOmitted_raw-images
v20
Jun 22, 2022
raw-images_allClasses
v19
Jun 22, 2022
bug-fix_TEST
v15
Jun 16, 2022
2022-05-31 2:21pm
v9
May 31, 2022
2022-05-31 tiling 4 -no resizing-
v8
May 31, 2022
2022-05-31 tiling 3
v7
May 31, 2022
2022-05-31 with tiling 2
v6
May 31, 2022
2022-05-31 with tiling
v5
May 31, 2022
2022-05-31
v4
May 31, 2022
joyce_2022-05-24
v3
May 24, 2022
joyce_drop_class
v2
May 20, 2022
joyce_data
v1
May 20, 2022
v30
Full images
Generated on Jan 6, 2023
Popular Download Formats
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.
MT-YOLOv6
MT-YOLOv6 TXT annotations used with meituan/YOLOv6.
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.
CreateML JSON
CreateML JSON format is used with Apple's CreateML and Turi Create tools.
Other Formats
Choose another format.
232 Total Images
View All Images
Dataset Split
Train Set 96%
223Images
Valid Set 4%
9Images
Test Set %
0Images
Preprocessing
Auto-Orient: Applied
Resize: Stretch to 416x416
Modify Classes: 0 remapped, 1 dropped
Augmentations
Outputs per training example: 5
Flip: Horizontal, Vertical
Brightness: Between -21% and +21%
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