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segformer-b0-finetuned-segments-sidewalk-2

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PyTorch
segformer
vision
semantic-segmentation
Generated from Trainer
Inference Endpoints
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@@ -14,12 +14,12 @@ 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 None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.6110
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- - Mean Iou: 0.1367
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- - Mean Accuracy: 0.1875
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- - Overall Accuracy: 0.6881
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- - Per Category Iou: [nan, 0.4358992825292338, 0.7376212533796545, 0.0, 0.02621246785982382, 0.00043211553703970075, nan, 8.015736841193088e-05, 0.0, 0.0, 0.5772424406028427, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.5033973978720222, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.6694084376012885, 0.36677387384992766, 0.7828643395512115, 0.0, 0.0, 0.0, 0.0]
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- - Per Category Accuracy: [nan, 0.7389109053459296, 0.9324628469379894, 0.0, 0.02623033308234139, 0.0004332220803205606, nan, 8.024933685852042e-05, 0.0, 0.0, 0.8439578372191763, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.8847599631606713, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.9401313327814339, 0.38739120652321696, 0.8720917833707338, 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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- | 1.9298 | 0.5 | 100 | 1.7681 | 0.1199 | 0.1738 | 0.6593 | [nan, 0.40090810552182926, 0.7239864361905679, 0.0, 0.0017647225539722043, 0.00039874720019120934, nan, 0.0001017888855199896, 0.0, 0.0, 0.5266858358368541, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.4982673421879428, 0.0, 0.00013373579730320418, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.6162371375150854, 0.06163531325154459, 0.7657580090286384, 0.0, 0.0, 0.00018678310951226077, 0.0] | [nan, 0.7294785690321806, 0.9040101249330418, 0.0, 0.001765437619648865, 0.00039998860566583264, nan, 0.0001019383468202827, 0.0, 0.0, 0.8602156135201492, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.8580331196294607, 0.0, 0.00013373747781887638, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.9448016748237693, 0.06210387463397946, 0.852450875265527, 0.0, 0.0, 0.0001871709770030242, 0.0] |
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- | 2.0284 | 1.0 | 200 | 1.6110 | 0.1367 | 0.1875 | 0.6881 | [nan, 0.4358992825292338, 0.7376212533796545, 0.0, 0.02621246785982382, 0.00043211553703970075, nan, 8.015736841193088e-05, 0.0, 0.0, 0.5772424406028427, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.5033973978720222, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.6694084376012885, 0.36677387384992766, 0.7828643395512115, 0.0, 0.0, 0.0, 0.0] | [nan, 0.7389109053459296, 0.9324628469379894, 0.0, 0.02623033308234139, 0.0004332220803205606, nan, 8.024933685852042e-05, 0.0, 0.0, 0.8439578372191763, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.8847599631606713, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.9401313327814339, 0.38739120652321696, 0.8720917833707338, 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 None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.5568
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+ - Mean Iou: 0.1429
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+ - Mean Accuracy: 0.1909
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+ - Overall Accuracy: 0.7302
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+ - Per Category Iou: [nan, 0.4939249651377763, 0.7719350693388762, 0.0, 0.03491527266588522, 0.0007851043658245269, nan, 0.0, 0.0, 0.0, 0.5957492947164502, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5578896542272563, 0.0, 8.731498772678703e-06, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.763823679694206, 0.5627622811191442, 0.7914659567091414, 0.0, 0.0, 3.4412391213828277e-06, 0.0]
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+ - Per Category Accuracy: [nan, 0.8311952182095418, 0.9317484161484766, 0.0, 0.03491984657702897, 0.0007870032398194515, nan, 0.0, 0.0, 0.0, 0.8874888349993965, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8733272803927883, 0.0, 8.732022947756307e-06, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.9141159811565875, 0.788047139121296, 0.8472913943123015, 0.0, 0.0, 3.4413693897075525e-06, 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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+ | 2.1501 | 0.5 | 100 | 1.7902 | 0.1355 | 0.1841 | 0.7104 | [nan, 0.45744119291698754, 0.7571272493181429, 0.0, 0.00033640367932780314, 0.0003751385454855486, nan, 1.1301520807983395e-05, 0.0, 0.0, 0.603134432234208, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5277902723725074, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.7525759481063863, 0.5102277252814887, 0.7259971515863731, 0.0, 0.0, 0.0, 0.0] | [nan, 0.8342415185752861, 0.8927152239695044, 0.0, 0.00033640367932780314, 0.00037609004380752546, nan, 1.1301659177747787e-05, 0.0, 0.0, 0.8649657094576059, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.9071099090067092, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.9070157155961565, 0.719362898131699, 0.7640401968695343, 0.0, 0.0, 0.0, 0.0] |
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+ | 1.5623 | 1.0 | 200 | 1.5568 | 0.1429 | 0.1909 | 0.7302 | [nan, 0.4939249651377763, 0.7719350693388762, 0.0, 0.03491527266588522, 0.0007851043658245269, nan, 0.0, 0.0, 0.0, 0.5957492947164502, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5578896542272563, 0.0, 8.731498772678703e-06, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.763823679694206, 0.5627622811191442, 0.7914659567091414, 0.0, 0.0, 3.4412391213828277e-06, 0.0] | [nan, 0.8311952182095418, 0.9317484161484766, 0.0, 0.03491984657702897, 0.0007870032398194515, nan, 0.0, 0.0, 0.0, 0.8874888349993965, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8733272803927883, 0.0, 8.732022947756307e-06, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.9141159811565875, 0.788047139121296, 0.8472913943123015, 0.0, 0.0, 3.4413693897075525e-06, 0.0] |
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  ### Framework versions