Published April 11, 2023
| Version v1
Dataset
Open
Deep Learning for Microfluidic Assisted Caenorhabditis elegans Multi-parameter Identification Using YOLOv7
Authors/Creators
Description
The effectiveness of deep learning model relies on a large number of labeled image datasets. We have collected a dataset of 3373 worm images from microfluidic devices in various studies as datasets. Then, the datasets were labeled for training methods. The acquired videos were continuously intercepted at 10-frame intervals to capture cropped worm images. Totally 3931 images were extract. Worms in each image were manually annotated using LabelImg. This VOC-format annotation tool generated Extensible Markup Language (XML) files for the model. There were 2426 labels for the WT category and 1505 for the GFP category.
Files
1000000.000.jpg
Files
(51.9 MB)
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md5:61832c8fc8ed977540a1c08887b4926f
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1.5 kB | Preview Download |
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md5:ea36821d04489817fd24bf397fae1f78
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4.9 kB | Preview Download |
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md5:3b73f1cb43531f8a2554c1833f18d985
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1.5 kB | Preview Download |
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md5:9e1842518a0b785cb7c7e006c5e54549
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5.0 kB | Preview Download |
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md5:bc77c265cef88d5735b84e97a667ba6c
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1.5 kB | Preview Download |
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md5:42b77b0e29f2de2a866e94a63063baae
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5.0 kB | Preview Download |
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md5:d9aa7f69c85ce7c391aee7fdb2b72268
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1.5 kB | Preview Download |
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md5:e96e91f616beca07ef2c3f70fa62cdb0
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5.0 kB | Preview Download |
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md5:afd10a9f884e74e12a3e28fd5374c0d9
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1.5 kB | Preview Download |
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md5:1891c0f381a22953f8dc5956d356c040
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4.8 kB | Preview Download |
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md5:7d3e22796c0ce08061feea61e288fb5a
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1.5 kB | Preview Download |
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md5:caa8cf83c84d534a09d8749177c8de71
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4.6 kB | Preview Download |
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md5:497b6ef10714c1003eead8f9988dd25c
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1.5 kB | Preview Download |
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md5:33664b646a04d66ec55e418f5fb14974
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4.2 kB | Preview Download |
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md5:0c0f88bdf9e6f8e2fc114f235c0bd9d5
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1.0 kB | Preview Download |
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md5:c0c26934c745d6d7b189babfc88b4f71
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4.2 kB | Preview Download |
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md5:e9a23ba0fd92f0291bc2068224e86840
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1.0 kB | Preview Download |
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md5:f6f874b9226f22ffaf2f04fffd5805d4
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4.1 kB | Preview Download |
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md5:77ddba62cc355773aa6ae967a8dae64d
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1.0 kB | Preview Download |
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md5:a4a908acf79a4464ae5be48480e854b2
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6.1 kB | Preview Download |
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md5:918d5613f660e90dd9b28b68db130984
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544 Bytes | Preview Download |
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md5:b98622428c960b0e4807b056af94b2d8
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5.8 kB | Preview Download |
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md5:a6d9a9c76517041a542596e7a2a7b037
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544 Bytes | Preview Download |
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md5:e877dcfd2a73aba5e77c1fea301ca1ee
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5.8 kB | Preview Download |
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md5:cb46904cf7b6253432f5830b0360f518
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544 Bytes | Preview Download |
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md5:a7588f3c6762bd86e916eadebdb0dae3
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5.9 kB | Preview Download |
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md5:34e1a78827692785fe16702510cdc7e5
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544 Bytes | Preview Download |
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md5:2d69c4b2540dd8d645ad2560d5c97ff8
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5.7 kB | Preview Download |
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md5:2dbfda5be107ae690ca63dc9126a1b10
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544 Bytes | Preview Download |
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md5:c1442c6ab2515f38265e85310b7347b8
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5.6 kB | Preview Download |
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md5:2d5d7375973772e725d2e820546eeab3
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544 Bytes | Preview Download |
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md5:0840f2df79da3994f7e27564786c016f
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5.9 kB | Preview Download |
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md5:e41c740344cb79965c917814b584632a
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544 Bytes | Preview Download |
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md5:81194e32e3b7d446875ddfb7cc4628eb
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5.7 kB | Preview Download |
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md5:d3bde0d64bbba16a697af28df79a1f83
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544 Bytes | Preview Download |
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md5:e5038688a520c5ea87b18792441d5d9a
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5.6 kB | Preview Download |
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md5:5fbd7d5c26bb10b2d865e1a5bd418699
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544 Bytes | Preview Download |
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md5:78d82ac3304e45b483d17368b91ed986
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5.3 kB | Preview Download |
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md5:021a109da8d6044dbc9bc8c3f689f7f6
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545 Bytes | Preview Download |
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md5:e315180ce14d087bbd091e5491b15754
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5.7 kB | Preview Download |
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md5:f328e970e4f21edd95391c4da109c794
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544 Bytes | Preview Download |
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md5:0c8ce21ddd14a4c706e6d228cd726516
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6.4 kB | Preview Download |
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md5:cf3e21a43db7d4308edeb511c3946b02
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544 Bytes | Preview Download |
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md5:1bdf8734455e4a5f5db8379538998879
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6.0 kB | Preview Download |
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md5:bd89bcaa9831858b6a0b8e43b59f997e
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544 Bytes | Preview Download |
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md5:fe5cb3d06c90110344e701e78fa6729d
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6.0 kB | Preview Download |
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md5:76e9fbb6591bc69e87595f7e8182fad7
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544 Bytes | Preview Download |
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md5:86e35a264765cf3323d7f75d70131351
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6.4 kB | Preview Download |
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md5:1e6841cbb0240be031af8001b0e5e3bc
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544 Bytes | Preview Download |
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md5:2df13fdaf352f8091fe113c7693122d4
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5.8 kB | Preview Download |
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md5:6984e538ef119500d810523deed6d7f6
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544 Bytes | Preview Download |
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md5:b664aeed1602ad7508a148cfa53c50f7
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6.1 kB | Preview Download |
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md5:f159d18178e51534da7e7dbfc2fe116c
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544 Bytes | Preview Download |
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md5:30bfcfa4d2ab500309c980775563a8ca
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6.1 kB | Preview Download |
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md5:65e3c726737d84029bff2681b972776e
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544 Bytes | Preview Download |
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md5:ddba4da3eedc7d30fefd52de620a52d3
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6.0 kB | Preview Download |
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md5:eeb6fbc8f392e9d2e210564cde6eba8d
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544 Bytes | Preview Download |
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md5:e8c0da50b38f0acb7e87bfe713eb16f0
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544 Bytes | Preview Download |
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md5:21a01de205a5deb3cac5ad97002aa9c1
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544 Bytes | Preview Download |
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md5:fc170705f48694382836df235a2032c1
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544 Bytes | Preview Download |
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md5:cefec88ec57303146e4ce38b25aeab51
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544 Bytes | Preview Download |
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md5:244c6a77edb19364867abe4e71c9470d
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82.9 kB | Preview Download |
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md5:1224d6534c603ea3046c765e67fe42c6
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585 Bytes | Preview Download |
