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SegFormer_mit-b5_Clean-Set3-Grayscale

Image Segmentation
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metadata
license: other
base_model: nvidia/mit-b5
tags:
  - generated_from_trainer
model-index:
  - name: SegFormer_mit-b5_Clean-Set3-Grayscale
    results: []
pipeline_tag: image-segmentation

SegFormer_mit-b5_Clean-Set3-Grayscale

This model is a fine-tuned version of nvidia/mit-b5 on _Clean-Set3-Grayscale. It achieves the following results on the evaluation set:

  • Train-Loss: 0.0053
  • Loss: 0.0156
  • Mean Iou: 0.9776
  • Mean Accuracy: 0.9882
  • Overall Accuracy: 0.9952
  • Accuracy Background: 0.9974
  • Accuracy Melt: 0.9708
  • Accuracy Substrate: 0.9963
  • Iou Background: 0.9942
  • Iou Melt: 0.9458
  • Iou Substrate: 0.9927

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: 0.0002
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 200
  • num_epochs: 50

Training results

Training Loss Epoch Step Validation Loss Mean Iou Mean Accuracy Overall Accuracy Accuracy Background Accuracy Melt Accuracy Substrate Iou Background Iou Melt Iou Substrate
0.1206 1.8519 50 0.0898 0.8826 0.9277 0.9727 0.9809 0.8182 0.9840 0.9697 0.7209 0.9571
0.0687 3.7037 100 0.0445 0.9291 0.9568 0.9845 0.9920 0.8888 0.9895 0.9833 0.8286 0.9754
0.0457 5.5556 150 0.0413 0.9284 0.9428 0.9859 0.9938 0.8381 0.9966 0.9877 0.8204 0.9770
0.0281 7.4074 200 0.0240 0.9592 0.9706 0.9914 0.9971 0.9198 0.9949 0.9900 0.9011 0.9865
0.0234 9.2593 250 0.0179 0.9672 0.9810 0.9932 0.9960 0.9513 0.9957 0.9926 0.9195 0.9893
0.0147 11.1111 300 0.0180 0.9672 0.9785 0.9932 0.9955 0.9429 0.9972 0.9925 0.9197 0.9893
0.012 12.9630 350 0.0139 0.9748 0.9864 0.9946 0.9967 0.9664 0.9962 0.9936 0.9390 0.9918
0.0104 14.8148 400 0.0138 0.9756 0.9890 0.9947 0.9972 0.9748 0.9949 0.9935 0.9413 0.9919
0.0094 16.6667 450 0.0136 0.9767 0.9862 0.9950 0.9965 0.9646 0.9974 0.9940 0.9436 0.9924
0.0101 18.5185 500 0.0135 0.9767 0.9867 0.9950 0.9974 0.9663 0.9964 0.9940 0.9438 0.9924
0.0087 20.3704 550 0.0144 0.9764 0.9887 0.9949 0.9954 0.9736 0.9970 0.9935 0.9435 0.9923
0.0078 22.2222 600 0.0145 0.9760 0.9885 0.9949 0.9967 0.9727 0.9960 0.9938 0.9417 0.9924
0.0095 24.0741 650 0.0145 0.9753 0.9855 0.9948 0.9971 0.9626 0.9967 0.9939 0.9398 0.9921
0.0073 25.9259 700 0.0145 0.9761 0.9892 0.9949 0.9965 0.9752 0.9960 0.9938 0.9419 0.9925
0.009 27.7778 750 0.0143 0.9772 0.9891 0.9951 0.9958 0.9745 0.9970 0.9938 0.9451 0.9929
0.0049 29.6296 800 0.0143 0.9782 0.9883 0.9953 0.9966 0.9713 0.9971 0.9942 0.9474 0.9929
0.0075 31.4815 850 0.0153 0.9767 0.9886 0.9951 0.9967 0.9727 0.9963 0.9941 0.9434 0.9925
0.008 33.3333 900 0.0155 0.9772 0.9876 0.9952 0.9970 0.9690 0.9968 0.9943 0.9447 0.9927
0.0061 35.1852 950 0.0150 0.9777 0.9877 0.9953 0.9973 0.9691 0.9967 0.9943 0.9461 0.9928
0.0053 37.0370 1000 0.0156 0.9776 0.9882 0.9952 0.9974 0.9708 0.9963 0.9942 0.9458 0.9927

Framework versions

  • Transformers 4.41.2
  • Pytorch 2.0.1+cu117
  • Datasets 2.19.2
  • Tokenizers 0.19.1