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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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metadata
license: other
tags:
  - 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 None dataset. It achieves the following results on the evaluation set:

  • Loss: 1.6110
  • Mean Iou: 0.1367
  • Mean Accuracy: 0.1875
  • Overall Accuracy: 0.6881
  • 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]
  • 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]

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: 4
  • eval_batch_size: 4
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 1

Training results

Training Loss Epoch Step Validation Loss Mean Iou Mean Accuracy Overall Accuracy Per Category Iou Per Category Accuracy
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]
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]

Framework versions

  • Transformers 4.21.1
  • Pytorch 1.12.1
  • Datasets 2.4.0
  • Tokenizers 0.12.1