Raccoon Image Dataset
Versions
2021-05-05 2:58am
v40
May 5, 2021
raccons-modelplace
v39
Apr 30, 2021
416x416-resize
v38
Feb 10, 2021
416x416-resize2
v37
Feb 10, 2021
500x500-augmentations
v34
May 31, 2020
resize500x500-augmentations
v33
May 31, 2020
grant-ex
v32
Feb 29, 2020
noise-only-6
v31
Feb 28, 2020
noise-only
v30
Feb 28, 2020
gray-contrast-noise
v29
Feb 28, 2020
gray-noise
v28
Feb 28, 2020
another-one
v27
Feb 28, 2020
example-less-contrast
v26
Feb 28, 2020
example-dan
v25
Feb 28, 2020
reflect-800x400
v24
Feb 25, 2020
2020-02-25 1:38pm
v23
Feb 25, 2020
reflect-801x400
v22
Feb 25, 2020
2020-02-25 1:35pm
v21
Feb 25, 2020
reflect-800x400
v20
Feb 25, 2020
reflect-800x400
v19
Feb 25, 2020
original
v18
Feb 25, 2020
reflect-test
v17
Feb 25, 2020
fill-416x416-crop-50
v16
Feb 23, 2020
crop-50
v15
Feb 23, 2020
horizontal-shear-15
v14
Feb 23, 2020
horizontal-shear-15
v13
Feb 22, 2020
hist-eq
v12
Feb 22, 2020
reflect-800x400
v11
Feb 22, 2020
resize-reflect
v10
Feb 22, 2020
resize416x416
v9
Feb 22, 2020
resize416x416
v8
Feb 22, 2020
resize416x416
v7
Feb 22, 2020
resize-reflect-annotations
v6
Feb 22, 2020
center-crop-in
v5
Feb 22, 2020
416x416-resize
v4
Feb 22, 2020
center-crop-in
v3
Feb 22, 2020
raw
v2
Feb 14, 2020
v32
grant-ex
Generated on Feb 29, 2020
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.
496 Total Images
View All Images
Dataset Split
Train Set 91%
450Images
Valid Set 6%
29Images
Test Set 3%
17Images
Preprocessing
Resize: Fill (with center crop) in 500x500
Augmentations
Outputs per training example: 3
Flip: Horizontal, Vertical
Rotation: Between -20° and +20°
Exposure: Between -25% and +25%
Noise: Up to 3% of pixels
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