Dataset Versions

Versions
2023-03-15 4:15pm
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2023-03-15 4:15pm
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2023-03-15 4:12pm
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2023-03-05 10:55pm
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2023-03-05 10:46pm
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2023-03-05 10:42pm
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2023-03-05 10:38pm
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2023-03-05 10:31pm
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2023-03-05 10:23pm
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2023-03-05 10:08pm
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2023-03-02 11:41pm
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2023-03-02 11:34pm
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2023-03-02 11:30pm
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2023-03-02 11:26pm
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2023-03-02 11:26pm
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2023-03-02 11:26pm
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2023-03-02 11:26pm
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2023-03-02 11:20pm
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2023-03-02 11:16pm
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2023-03-02 11:09pm
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2023-03-02 11:09pm
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2023-03-02 9:57pm
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2023-03-02 9:48pm
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2023-03-02 9:47pm
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2023-03-02 10:51am
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2023-03-01 11:54pm
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2023-03-01 11:43pm
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2023-03-01 11:17pm
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2023-03-01 11:15pm
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2023-03-01 10:36pm
v7
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2023-03-01 8:17pm
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2023-03-01 5:27pm
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2023-03-01 5:01pm
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2023-02-12 9:03pm
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2023-02-12 8:26pm
v1
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2 years ago
v34

2023-03-05 10:46pm

Generated on Mar 5, 2023

Popular Download Formats

Pascal VOC XML
Common XML annotation format for local data munging (pioneered by ImageNet).
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.

Dataset Split

Train Set 97%
300Images
Valid Set 2%
5Images
Test Set 1%
3Images

Preprocessing

Auto-Orient: Applied
Static Crop: 25-75% Horizontal Region, 25-75% Vertical Region
Resize: Stretch to 640x640
Filter Null: Require all images to contain annotations.

Augmentations

Outputs per training example: 2
Saturation: Between -25% and +25%
Noise: Up to 1% of pixels
Bounding Box: Rotation: Between -5° and +5°

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