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segformer-b0-finetuned-segments-sidewalk-2

Transformers
PyTorch
segformer
vision
semantic-segmentation
Generated from Trainer
Inference Endpoints
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@@ -19,12 +19,12 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.6449
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- - Mean Iou: 0.1548
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- - Mean Accuracy: 0.2076
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- - Overall Accuracy: 0.7095
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- - Per Category Iou: [nan, 0.5140599138533896, 0.7174504614949924, 0.0, 0.2891488100731331, 0.0017519579090739337, nan, 2.896471421964715e-05, 0.0, 0.0, 0.572608873148249, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.5022343403146414, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.7363557838688449, 0.5136205023614682, 0.7964013451546083, 0.0, 0.0, 0.0, 0.0]
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- - Per Category Accuracy: [nan, 0.8064241561480351, 0.9062036975406429, 0.0, 0.30153947289328054, 0.0017733699866858271, nan, 2.8972126882463944e-05, 0.0, 0.0, 0.8850507869466231, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.8639780836322506, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.9144389240012535, 0.668388685537115, 0.8810818896148822, 0.0, 0.0, 0.0, 0.0]
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  ## Model description
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@@ -53,10 +53,10 @@ The following hyperparameters were used during training:
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  ### Training results
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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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- | 1.9382 | 0.5 | 100 | 1.8048 | 0.1339 | 0.1853 | 0.6789 | [nan, 0.4752344017185758, 0.694067134540047, 0.0, 0.12409993164299513, 0.0008311245506368295, nan, 0.0013254481219605065, 0.0, 0.0, 0.5409277406718473, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.4841581403327513, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.690024139245331, 0.2320795469313362, 0.7749690806204844, 0.0, 0.0, 0.0, 0.0] | [nan, 0.7639758158865736, 0.8976245985998512, 0.0, 0.12474373026419283, 0.0008415168706412761, nan, 0.0013278891487795974, 0.0, 0.0, 0.880573362170307, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.8526119656818829, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.9393214014210579, 0.26355731572405905, 0.8357039981550286, 0.0, 0.0, 0.0, 0.0] |
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- | 1.5173 | 1.0 | 200 | 1.6449 | 0.1548 | 0.2076 | 0.7095 | [nan, 0.5140599138533896, 0.7174504614949924, 0.0, 0.2891488100731331, 0.0017519579090739337, nan, 2.896471421964715e-05, 0.0, 0.0, 0.572608873148249, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.5022343403146414, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.7363557838688449, 0.5136205023614682, 0.7964013451546083, 0.0, 0.0, 0.0, 0.0] | [nan, 0.8064241561480351, 0.9062036975406429, 0.0, 0.30153947289328054, 0.0017733699866858271, nan, 2.8972126882463944e-05, 0.0, 0.0, 0.8850507869466231, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.8639780836322506, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.9144389240012535, 0.668388685537115, 0.8810818896148822, 0.0, 0.0, 0.0, 0.0] |
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  ### Framework versions
 
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  This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.5766
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+ - Mean Iou: 0.1371
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+ - Mean Accuracy: 0.1845
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+ - Overall Accuracy: 0.7137
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+ - Per Category Iou: [nan, 0.4878460273549076, 0.7555073936058639, 0.0, 0.021023119916983492, 6.661075803708754e-07, nan, 0.0, 0.0, 0.0, 0.5945035814400078, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.5297440422960749, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.7198551864914086, 0.489491922020114, 0.7904739581298643, 0.0, 0.0, 0.0, 0.0]
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+ - Per Category Accuracy: [nan, 0.8122402393699828, 0.916307187222316, 0.0, 0.02103704204254936, 6.661204478993891e-07, nan, 0.0, 0.0, 0.0, 0.8989685161351728, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.8771164563053133, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.9407576559617489, 0.6174625729307718, 0.821407178606353, 0.0, 0.0, 0.0, 0.0]
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  ## Model description
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  ### Training results
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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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+ | 1.7378 | 0.5 | 100 | 1.8155 | 0.1247 | 0.1711 | 0.6916 | [nan, 0.46555602373647126, 0.7353888954834877, 0.0, 0.009228814643632902, 0.00010122529220478676, nan, 0.0, 0.0, 0.0, 0.5739398587525921, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.5094931557231258, 0.0, 5.3423644732762526e-05, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.6692211552011066, 0.2651198767217891, 0.7633341956468725, 0.0, 0.0, 0.0, 0.0] | [nan, 0.7515868666775908, 0.9079116468110205, 0.0, 0.009233668577520995, 0.00010125030808070715, nan, 0.0, 0.0, 0.0, 0.8690972710699856, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.8791886402701642, 0.0, 5.361129513400909e-05, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.9598752209264458, 0.3079896302845186, 0.7892434570182117, 0.0, 0.0, 0.0, 0.0] |
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+ | 1.6547 | 1.0 | 200 | 1.5766 | 0.1371 | 0.1845 | 0.7137 | [nan, 0.4878460273549076, 0.7555073936058639, 0.0, 0.021023119916983492, 6.661075803708754e-07, nan, 0.0, 0.0, 0.0, 0.5945035814400078, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.5297440422960749, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.7198551864914086, 0.489491922020114, 0.7904739581298643, 0.0, 0.0, 0.0, 0.0] | [nan, 0.8122402393699828, 0.916307187222316, 0.0, 0.02103704204254936, 6.661204478993891e-07, nan, 0.0, 0.0, 0.0, 0.8989685161351728, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.8771164563053133, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.9407576559617489, 0.6174625729307718, 0.821407178606353, 0.0, 0.0, 0.0, 0.0] |
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  ### Framework versions