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README.md
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---
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license: apache-2.0
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tags:
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- vision
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- image-segmentation
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- generated_from_trainer
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model-index:
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- name: segformer-b0-finetuned-segments-sidewalk-2
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# segformer-b0-finetuned-segments-sidewalk-2
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This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the userGagan/ResizedSample dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3429
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- Mean Iou: 0.8143
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- Mean Accuracy: 0.9007
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- Overall Accuracy: 0.9061
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- Per Category Iou: [0.8822819675417668, 0.7774253195321242, 0.7832033563111727]
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- Per Category Accuracy: [0.9319684170082266, 0.8657193844491432, 0.9044945609610779]
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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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: 2
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- eval_batch_size: 2
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 5
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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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| 0.7949 | 0.5 | 20 | 0.8960 | 0.7129 | 0.8533 | 0.8427 | [0.7978191889735743, 0.6994730230171242, 0.6413103816527537] | [0.826874349660607, 0.8237981626592454, 0.9091007880329902] |
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| 0.4881 | 1.0 | 40 | 0.6195 | 0.7364 | 0.8610 | 0.8552 | [0.8041892620489134, 0.6981663805103046, 0.7069887055480671] | [0.8308827565320059, 0.887905283397269, 0.8642919506720577] |
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| 0.3115 | 1.5 | 60 | 0.4767 | 0.7352 | 0.8536 | 0.8588 | [0.8276338695141907, 0.7016825436162023, 0.6763414045904438] | [0.8633649830215921, 0.8776778472775076, 0.8196451790592317] |
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| 0.5863 | 2.0 | 80 | 0.4895 | 0.7543 | 0.8748 | 0.8668 | [0.8156517914197925, 0.7259786638902507, 0.7213518497027839] | [0.8402281798360435, 0.8932153836673491, 0.8909222571543128] |
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| 0.5182 | 2.5 | 100 | 0.4058 | 0.7904 | 0.8866 | 0.8919 | [0.860991170688589, 0.7583876635226005, 0.7518265397248736] | [0.9088903949664655, 0.8761789935147187, 0.8746304338865427] |
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| 0.4755 | 3.0 | 120 | 0.3683 | 0.7896 | 0.8861 | 0.8895 | [0.8547537413009911, 0.7465075384127533, 0.7674680941571024] | [0.8979683913158062, 0.8865259395690547, 0.8738060532025316] |
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| 0.6616 | 3.5 | 140 | 0.3697 | 0.7915 | 0.8874 | 0.8898 | [0.8551700094228354, 0.7431970428539307, 0.7761922571371438] | [0.8899387313627766, 0.903193218309171, 0.8690639906770039] |
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| 0.5087 | 4.0 | 160 | 0.3367 | 0.8061 | 0.8987 | 0.8987 | [0.8640367246398447, 0.7643869962764198, 0.7899951558528526] | [0.9012200396208266, 0.8918889478830869, 0.902900133774502] |
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| 0.5478 | 4.5 | 180 | 0.3297 | 0.8131 | 0.8991 | 0.9040 | [0.8775309087721331, 0.7692790103652185, 0.792538025793261] | [0.9196387801394476, 0.8895118205906903, 0.8882327151727265] |
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| 0.389 | 5.0 | 200 | 0.3429 | 0.8143 | 0.9007 | 0.9061 | [0.8822819675417668, 0.7774253195321242, 0.7832033563111727] | [0.9319684170082266, 0.8657193844491432, 0.9044945609610779] |
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### Framework versions
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- Transformers 4.20.1
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- Pytorch 1.12.0+cu113
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- Datasets 2.3.2
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- Tokenizers 0.12.1
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