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segformer-b2-finetuned-segments-pv_v1_normalized_p100_4batch_fp

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segformer-b2-finetuned-segments-pv_v1_normalized_p100_4batch_fp

This model is a fine-tuned version of nvidia/segformer-b2-finetuned-ade-512-512 on the mouadenna/satellite_PV_dataset_train_test_v1 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0046
  • Mean Iou: 0.8880
  • Precision: 0.9115

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: 1e-05
  • train_batch_size: 4
  • eval_batch_size: 4
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.01
  • num_epochs: 40
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Mean Iou Precision
0.6668 0.9989 229 0.4009 0.5075 0.5321
0.2583 1.9978 458 0.1436 0.6208 0.6535
0.1355 2.9967 687 0.0809 0.7078 0.7644
0.088 4.0 917 0.0585 0.7472 0.8136
0.0638 4.9989 1146 0.0452 0.7737 0.8353
0.05 5.9978 1375 0.0365 0.7845 0.8394
0.0401 6.9967 1604 0.0344 0.8087 0.8717
0.0332 8.0 1834 0.0277 0.8128 0.8682
0.0286 8.9989 2063 0.0188 0.8210 0.8710
0.0247 9.9978 2292 0.0148 0.8369 0.8881
0.0214 10.9967 2521 0.0133 0.8332 0.8716
0.0189 12.0 2751 0.0156 0.8286 0.8597
0.017 12.9989 2980 0.0139 0.8397 0.8726
0.0151 13.9978 3209 0.0154 0.8544 0.8943
0.0139 14.9967 3438 0.0114 0.8553 0.8897
0.0127 16.0 3668 0.0108 0.8517 0.8799
0.0118 16.9989 3897 0.0075 0.8658 0.9040
0.0108 17.9978 4126 0.0094 0.8700 0.9088
0.0101 18.9967 4355 0.0084 0.8746 0.9151
0.0094 20.0 4585 0.0071 0.8693 0.8973
0.0088 20.9989 4814 0.0071 0.8668 0.8931
0.0082 21.9978 5043 0.0060 0.8786 0.9151
0.008 22.9967 5272 0.0063 0.8776 0.9109
0.0075 24.0 5502 0.0066 0.8776 0.9052
0.0071 24.9989 5731 0.0060 0.8807 0.9115
0.0069 25.9978 5960 0.0062 0.8766 0.9004
0.0065 26.9967 6189 0.0059 0.8754 0.8963
0.0063 28.0 6419 0.0062 0.8825 0.9086
0.006 28.9989 6648 0.0050 0.8839 0.9101
0.0059 29.9978 6877 0.0051 0.8827 0.9069
0.0057 30.9967 7106 0.0056 0.8822 0.9053
0.0055 32.0 7336 0.0047 0.8866 0.9133
0.0055 32.9989 7565 0.0046 0.8876 0.9135
0.0053 33.9978 7794 0.0052 0.8839 0.9053
0.0052 34.9967 8023 0.0048 0.8828 0.9035
0.0051 36.0 8253 0.0046 0.8897 0.9156
0.005 36.9989 8482 0.0045 0.8891 0.9137
0.005 37.9978 8711 0.0047 0.8881 0.9120
0.005 38.9967 8940 0.0047 0.8879 0.9110
0.0049 39.9564 9160 0.0046 0.8880 0.9115

Framework versions

  • Transformers 4.42.3
  • Pytorch 2.1.2
  • Datasets 2.20.0
  • Tokenizers 0.19.1
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Evaluation results

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