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Ventalik
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segformer-roof

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PyTorch
segformer
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  1. README.md +17 -17
README.md CHANGED
@@ -15,12 +15,12 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [nvidia/mit-b3](https://huggingface.co/nvidia/mit-b3) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1331
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- - Mean Iou: 0.6705
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- - Mean Accuracy: 0.7459
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- - Overall Accuracy: 0.9509
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- - Per Category Iou: [0.9501174871140823, 0.44914356298751956, 0.612314004780354]
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- - Per Category Accuracy: [0.9835400780911927, 0.5547023488814158, 0.6996065789512536]
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 6e-05
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  - train_batch_size: 8
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  - eval_batch_size: 8
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  - seed: 42
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  | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------------------------------------------------:|:-------------------------------------------------------------:|
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- | 0.2357 | 1.0 | 930 | 0.1790 | 0.5725 | 0.6427 | 0.9370 | [0.9372296551214157, 0.265369517396097, 0.5150004888948623] | [0.9831748407739025, 0.30066901406516805, 0.6442311056295564] |
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- | 0.1732 | 2.0 | 1860 | 0.1700 | 0.5944 | 0.6623 | 0.9398 | [0.9401268036898566, 0.32807672100401025, 0.5150202791005837] | [0.9839554901228429, 0.3979733503813561, 0.6048359409441622] |
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- | 0.1559 | 3.0 | 2790 | 0.1600 | 0.6127 | 0.6753 | 0.9429 | [0.9427325757540327, 0.35965960312996503, 0.5357965624353673] | [0.9857619070576414, 0.448355090982288, 0.5917792010373596] |
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- | 0.1482 | 4.0 | 3720 | 0.1550 | 0.6070 | 0.6703 | 0.9437 | [0.944172393520638, 0.32282759103373876, 0.553957021364391] | [0.9867471837507854, 0.3693595842127839, 0.6546589242888398] |
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- | 0.1388 | 5.0 | 4650 | 0.1459 | 0.6224 | 0.6804 | 0.9463 | [0.9459092954263936, 0.3388316205746287, 0.582315028654454] | [0.9880828845164424, 0.3845685735297591, 0.6686608983285444] |
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- | 0.1311 | 6.0 | 5580 | 0.1462 | 0.6577 | 0.7466 | 0.9468 | [0.9461722360255241, 0.43412167635821636, 0.5928671376046599] | [0.9789455448939886, 0.5812880374397429, 0.679673538106026] |
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- | 0.1279 | 7.0 | 6510 | 0.1423 | 0.6611 | 0.7569 | 0.9469 | [0.9465891089044499, 0.4381184809600582, 0.5986930952368954] | [0.9773687408863051, 0.6022321705637107, 0.6909579192751797] |
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- | 0.1232 | 8.0 | 7440 | 0.1388 | 0.6682 | 0.7548 | 0.9491 | [0.9484426711464405, 0.44975791193706466, 0.6064948465370358] | [0.9802070573066378, 0.5898759789294347, 0.6944388397098907] |
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- | 0.1175 | 9.0 | 8370 | 0.1353 | 0.6665 | 0.7392 | 0.9505 | [0.9497153698000098, 0.44248964964215, 0.6074386624389524] | [0.9841990715683068, 0.5444332218950657, 0.6888275633995394] |
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- | 0.1174 | 10.0 | 9300 | 0.1331 | 0.6705 | 0.7459 | 0.9509 | [0.9501174871140823, 0.44914356298751956, 0.612314004780354] | [0.9835400780911927, 0.5547023488814158, 0.6996065789512536] |
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  ### Framework versions
 
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  This model is a fine-tuned version of [nvidia/mit-b3](https://huggingface.co/nvidia/mit-b3) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1424
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+ - Mean Iou: 0.6570
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+ - Mean Accuracy: 0.7338
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+ - Overall Accuracy: 0.9484
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+ - Per Category Iou: [0.9476783882507867, 0.42934908622698137, 0.5939862114533478]
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+ - Per Category Accuracy: [0.9826387957018243, 0.5398301458913112, 0.6788943731278633]
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 3e-05
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  - train_batch_size: 8
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  - eval_batch_size: 8
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  - seed: 42
 
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  | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------------------------------------------------:|:-------------------------------------------------------------:|
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+ | 0.2499 | 1.0 | 930 | 0.1815 | 0.5867 | 0.6662 | 0.9365 | [0.9366364285161356, 0.31719261329477677, 0.5063121008274123] | [0.9792975820886569, 0.391370646753873, 0.6280270152281657] |
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+ | 0.1757 | 2.0 | 1860 | 0.1714 | 0.6022 | 0.6773 | 0.9395 | [0.9393634573331426, 0.3425379991526554, 0.5248483866382888] | [0.9812229375223134, 0.428240950515148, 0.6224989524490145] |
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+ | 0.1594 | 3.0 | 2790 | 0.1629 | 0.6084 | 0.6710 | 0.9420 | [0.9416754823459575, 0.34843136321450174, 0.5350688263682425] | [0.9853655573464773, 0.43172953399691005, 0.5959325404903174] |
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+ | 0.1523 | 4.0 | 3720 | 0.1596 | 0.6076 | 0.6748 | 0.9431 | [0.9431751130845217, 0.3239168742399479, 0.5556505610312258] | [0.9852091404732521, 0.3708811641866297, 0.6682165559673183] |
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+ | 0.1439 | 5.0 | 4650 | 0.1523 | 0.6302 | 0.7055 | 0.9446 | [0.9440239408727975, 0.37488313572379306, 0.5716850871490047] | [0.9823225423399531, 0.45512728622828424, 0.6790873748340986] |
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+ | 0.1371 | 6.0 | 5580 | 0.1507 | 0.6435 | 0.7255 | 0.9454 | [0.9448592138832133, 0.40938956878632793, 0.5763842195624918] | [0.9805292644632151, 0.5269108861841263, 0.6691477422245232] |
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+ | 0.1353 | 7.0 | 6510 | 0.1483 | 0.6535 | 0.7471 | 0.9454 | [0.945006495944485, 0.428182455906198, 0.5872114398937206] | [0.977372124880732, 0.5920333447023434, 0.6719542751488515] |
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+ | 0.1313 | 8.0 | 7440 | 0.1475 | 0.6543 | 0.7416 | 0.9465 | [0.9458449483191159, 0.4282098022538787, 0.5888713805135868] | [0.9792916130278106, 0.5663642070609791, 0.6792639127879244] |
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+ | 0.1274 | 9.0 | 8370 | 0.1446 | 0.6511 | 0.7257 | 0.9477 | [0.9470057563004056, 0.41812909680083715, 0.5881565577705948] | [0.983095828320569, 0.5191575508625893, 0.6747574079503997] |
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+ | 0.1274 | 10.0 | 9300 | 0.1424 | 0.6570 | 0.7338 | 0.9484 | [0.9476783882507867, 0.42934908622698137, 0.5939862114533478] | [0.9826387957018243, 0.5398301458913112, 0.6788943731278633] |
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  ### Framework versions