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segformer-b0-finetuned-segments-sidewalk-2

Image Segmentation
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metadata
license: apache-2.0
tags:
  - vision
  - image-segmentation
  - generated_from_trainer
model-index:
  - name: segformer-b0-finetuned-segments-sidewalk-2
    results: []

segformer-b0-finetuned-segments-sidewalk-2

This model is a fine-tuned version of 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