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segformer_b1_finetuned_segment_pv_p100_16batch

This model is a fine-tuned version of nvidia/segformer-b1-finetuned-ade-512-512 on the mouadenna/satellite_PV_dataset_train_test_v1 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0062
  • Mean Iou: 0.8656
  • Precision: 0.9155

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: 0.00016
  • 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: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 40
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Mean Iou Precision
0.59 1.0 230 0.2289 0.5149 0.5478
0.111 2.0 460 0.0320 0.7322 0.8038
0.0254 3.0 690 0.0133 0.7865 0.8738
0.0115 4.0 920 0.0079 0.8335 0.8829
0.0078 5.0 1150 0.0076 0.8156 0.8598
0.0061 6.0 1380 0.0061 0.8436 0.8926
0.0051 7.0 1610 0.0056 0.8478 0.9170
0.0042 8.0 1840 0.0059 0.8497 0.8975
0.0038 9.0 2070 0.0062 0.8431 0.9186
0.0037 10.0 2300 0.0055 0.8529 0.9142
0.0036 11.0 2530 0.0061 0.8397 0.8834
0.0035 12.0 2760 0.0055 0.8497 0.8981
0.0032 13.0 2990 0.0055 0.8485 0.9015
0.0028 14.0 3220 0.0056 0.8549 0.8979
0.0028 15.0 3450 0.0059 0.8523 0.8975
0.0026 16.0 3680 0.0055 0.8579 0.9120
0.0026 17.0 3910 0.0056 0.8587 0.9110
0.0024 18.0 4140 0.0074 0.8295 0.9233
0.0029 19.0 4370 0.0058 0.8548 0.9092
0.0025 20.0 4600 0.0055 0.8556 0.8914
0.0025 21.0 4830 0.0054 0.8569 0.9017
0.0028 22.0 5060 0.0055 0.8622 0.9166
0.0024 23.0 5290 0.0057 0.8633 0.9216
0.0022 24.0 5520 0.0059 0.8623 0.9155
0.002 25.0 5750 0.0060 0.8614 0.9046
0.002 26.0 5980 0.0062 0.8563 0.9092
0.0019 27.0 6210 0.0059 0.8642 0.9125
0.0018 28.0 6440 0.0060 0.8656 0.9097
0.0018 29.0 6670 0.0060 0.8632 0.9174
0.0018 30.0 6900 0.0061 0.8647 0.9172
0.0018 31.0 7130 0.0062 0.8657 0.9155
0.0017 32.0 7360 0.0061 0.8650 0.9129
0.0017 33.0 7590 0.0062 0.8656 0.9138
0.0017 34.0 7820 0.0064 0.8657 0.9127
0.0016 35.0 8050 0.0065 0.8665 0.9156
0.0016 36.0 8280 0.0067 0.8624 0.9051
0.0015 37.0 8510 0.0065 0.8658 0.9116
0.0016 38.0 8740 0.0061 0.8660 0.9149
0.0015 39.0 8970 0.0063 0.8662 0.9155
0.0015 40.0 9200 0.0062 0.8656 0.9155

Framework versions

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