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segformer-b1-finetuned-segments-pv_v1_normalized_p100_4batch_try

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metadata
license: other
base_model: nvidia/segformer-b1-finetuned-ade-512-512
tags:
  - vision
  - image-segmentation
  - generated_from_trainer
metrics:
  - precision
model-index:
  - name: segformer-b1-finetuned-segments-pv_v1_normalized_p100_4batch_try
    results: []

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segformer-b1-finetuned-segments-pv_v1_normalized_p100_4batch_try

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: 0.0012
  • Mean Iou: 0.9586
  • Precision: 0.9787

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: 0.0004
  • 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: 0.1
  • num_epochs: 40
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Mean Iou Precision
0.2001 0.9989 229 0.0557 0.7433 0.8099
0.0234 1.9978 458 0.0111 0.8026 0.8735
0.0109 2.9967 687 0.0069 0.8264 0.9062
0.0077 4.0 917 0.0052 0.8659 0.9087
0.0053 4.9989 1146 0.0142 0.6652 0.6845
0.0055 5.9978 1375 0.0041 0.8885 0.9319
0.0041 6.9967 1604 0.0038 0.8855 0.9516
0.0046 8.0 1834 0.0041 0.8787 0.9430
0.0041 8.9989 2063 0.0035 0.8902 0.9105
0.0035 9.9978 2292 0.0028 0.9107 0.9475
0.0034 10.9967 2521 0.0027 0.9116 0.9390
0.0038 12.0 2751 0.0031 0.9001 0.9306
0.0033 12.9989 2980 0.0025 0.9160 0.9563
0.0029 13.9978 3209 0.0026 0.9153 0.9444
0.0026 14.9967 3438 0.0023 0.9218 0.9499
0.0027 16.0 3668 0.0027 0.9086 0.9525
0.0032 16.9989 3897 0.0029 0.9076 0.9439
0.0033 17.9978 4126 0.0024 0.9192 0.9459
0.0025 18.9967 4355 0.0027 0.9108 0.9538
0.0024 20.0 4585 0.0021 0.9276 0.9559
0.0022 20.9989 4814 0.0020 0.9316 0.9649
0.0021 21.9978 5043 0.0021 0.9287 0.9571
0.0025 22.9967 5272 0.0023 0.9217 0.9511
0.0022 24.0 5502 0.0020 0.9309 0.9676
0.002 24.9989 5731 0.0018 0.9360 0.9613
0.002 25.9978 5960 0.0017 0.9394 0.9621
0.0019 26.9967 6189 0.0017 0.9403 0.9664
0.0018 28.0 6419 0.0017 0.9405 0.9566
0.0017 28.9989 6648 0.0016 0.9438 0.9695
0.0017 29.9978 6877 0.0015 0.9469 0.9755
0.002 30.9967 7106 0.0016 0.9448 0.9672
0.0018 32.0 7336 0.0015 0.9459 0.9693
0.0016 32.9989 7565 0.0014 0.9486 0.9674
0.0015 33.9978 7794 0.0013 0.9528 0.9761
0.0015 34.9967 8023 0.0013 0.9520 0.9732
0.0014 36.0 8253 0.0013 0.9541 0.9705
0.0014 36.9989 8482 0.0012 0.9563 0.9739
0.0014 37.9978 8711 0.0012 0.9575 0.9764
0.0014 38.9967 8940 0.0012 0.9581 0.9766
0.0014 39.9564 9160 0.0012 0.9586 0.9787

Framework versions

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