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{ |
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"_id": { |
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"$oid": "652bbe8d115ff300cbd113e6" |
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}, |
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"network_id": "nanodet_plus_m_x1.5_416_imx500v1_mctq_Keras", |
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"date_inserted": { |
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"$date": { |
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"$numberLong": "1697371203072" |
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} |
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}, |
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"network_file": "/data/projects/swat/network_database/Tensorflow2/internal/nanodet-plus-m/nanodet-plus-m-x1.5-416_quant.keras", |
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"convpy_cli_supp": "", |
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"name": "nanodet_plus_m_x1.5_mctq", |
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"framework": "Tensorflow2", |
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"accuracy_measurements": [ |
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"Top1", |
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{ |
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"top1": 0.3316 |
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} |
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], |
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"background": false, |
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"dataset_id": "CocoPostprocessNANODET", |
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"preprocess": [ |
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{ |
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"ResizeBilinear": { |
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"name": "Resize", |
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"new_height": 416, |
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"new_width": 416 |
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} |
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}, |
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{ |
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"RGBtoBGR": { |
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"name": "RGBtoBGR" |
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} |
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}, |
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{ |
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"Normalize": { |
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"name": "Normalization", |
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"mean": [ |
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103.53, |
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116.28, |
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123.675 |
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], |
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57.375, |
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57.12, |
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58.395 |
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] |
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} |
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} |
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], |
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"RGB" |
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], |
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"outputs_type": [ |
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], |
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"quantization_type": "MCTQ" |
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} |