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---
license: apache-2.0
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b0-finetuned-segments-sidewalk-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b0-finetuned-segments-sidewalk-2
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the userGagan/ResizedSample dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3429
- Mean Iou: 0.8143
- Mean Accuracy: 0.9007
- Overall Accuracy: 0.9061
- Per Category Iou: [0.8822819675417668, 0.7774253195321242, 0.7832033563111727]
- Per Category Accuracy: [0.9319684170082266, 0.8657193844491432, 0.9044945609610779]
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------------------------------------------------:|:------------------------------------------------------------:|
| 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] |
| 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] |
| 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] |
| 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] |
| 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] |
| 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] |
| 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] |
| 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] |
| 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] |
| 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] |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
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