MIT Fishery

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MIT Fishery

Object Detection

MIT Fishery Image Dataset

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Versions
2023-07-19 8:40pm
v18
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a year ago
2023-07-14 11:05am
v17
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a year ago
2023-07-12 10:36pm
v16
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a year ago
2023-07-11 11:32pm
v15
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a year ago
2023-07-11 8:01am
v14
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a year ago
2023-07-10 2:49pm
v13
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a year ago
2023-07-10 9:35am
v12
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a year ago
2023-07-09 1:05pm
v11
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a year ago
2023-07-07 6:13am
v10
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a year ago
2023-07-06 9:20pm
v9
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a year ago
2023-07-06 9:06pm
v8
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a year ago
2023-07-05 9:32pm
v7
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a year ago
2023-07-03 8:51am
v6
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a year ago
2023-07-02 4:39pm
v5
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a year ago
2023-07-02 4:30pm
v4
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a year ago
2023-06-30 11:44am
v3
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a year ago
2023-06-30 8:00am
v2
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a year ago
2023-06-30 7:54am
v1
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a year ago
v9

2023-07-06 9:20pm

Generated on Jul 7, 2023
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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.
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4820 Total Images

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Dataset Split

Train Set 89%
4272Images
Valid Set 8%
371Images
Test Set 4%
177Images

Preprocessing

Auto-Orient: Applied
Resize: Fit within 320x240
Filter Null: Require at least 90% of images to contain annotations.

Augmentations

Outputs per training example: 3
Blur: Up to 2.5px
Cutout: 3 boxes with 10% size each
Bounding Box: Rotation: Between -5° and +5°
Bounding Box: Exposure: Between -15% and +15%
Bounding Box: Noise: Up to 5% of pixels