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raccoon-qnjzn/23 (latest)
Versions
2023-05-21 10:59pm
v24
·
2 years ago
2023-03-31 11:01pm
v23
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2 years ago
2023-02-20 7:08pm
v22
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2 years ago
2023-02-05 10:42pm
v21
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2 years ago
2023-01-24 8:54am
v20
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2 years ago
2023-01-20 10:41am
v19
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2 years ago
2023-01-15 2:50am
v18
·
2 years ago
2023-01-08 1:55pm
v17
·
2 years ago
Generated Small
v16
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2 years ago
2023-01-04 10:45pm
v15
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2 years ago
2023-01-04 10:34pm
v14
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2 years ago
2022-12-30 8:19pm
v13
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2 years ago
2022-12-23 2:10am
v12
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2 years ago
2022-12-20 3:41pm
v11
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2 years ago
2022-12-17 6:52pm
v10
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2 years ago
2022-12-15 8:16pm
v9
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2 years ago
2022-12-14 4:44pm
v8
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2 years ago
2022-12-12 6:27pm
v7
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2 years ago
2022-12-11 4:54pm
v6
·
2 years ago
2022-12-11 3:40pm
v5
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2 years ago
2022-12-08 8:48pm
v4
·
2 years ago
Raccoon_v3
v3
·
2 years ago
Raccoon_v2
v2
·
2 years ago
Raccoon_v1
v1
·
2 years ago
v18
2023-01-15 2:50am
Generated on
Jan 15, 2023
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.
3336 Total Images
View All Images
Dataset Split
Train
Set
90%
3005Images
Valid
Set
6%
208Images
Test
Set
4%
123Images
Preprocessing
Auto-Orient:
Applied
Resize:
Stretch to 640x640
Filter Null:
Require all images to contain annotations.
Augmentations
Outputs per training example:
2
Flip:
Horizontal
Rotation:
Between -9° and +9°
Grayscale:
Apply to 25% of images
