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autocrop-bilder

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metadata 
library_name: transformers
license: other
base_model: nvidia/mit-b0
tags:
  - image-segmentation
  - vision
  - generated_from_trainer
datasets:
  - generator
model-index:
  - name: autocrop-bilder
    results: []

autocrop-bilder

This model is a fine-tuned version of nvidia/mit-b0 on the /mnt/disk1/autocrop-data/datasets/bilder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0434
  • Mean Iou: 0.4950
  • Mean Accuracy: 0.9899
  • Overall Accuracy: 0.9899
  • Accuracy Background: nan
  • Accuracy Crop: 0.9899
  • Iou Background: 0.0
  • Iou Crop: 0.9899

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: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 0.1
  • num_epochs: 50.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Mean Iou Mean Accuracy Overall Accuracy Accuracy Background Accuracy Crop Iou Background Iou Crop
0.3277 1.0 112 0.3304 0.4474 0.8948 0.8948 nan 0.8948 0.0 0.8948
0.1834 2.0 224 0.1733 0.4725 0.9450 0.9450 nan 0.9450 0.0 0.9450
0.1279 3.0 336 0.1177 0.4907 0.9813 0.9813 nan 0.9813 0.0 0.9813
0.0879 4.0 448 0.0841 0.4929 0.9858 0.9858 nan 0.9858 0.0 0.9858
0.0796 5.0 560 0.0840 0.4871 0.9742 0.9742 nan 0.9742 0.0 0.9742
0.0641 6.0 672 0.0709 0.4930 0.9860 0.9860 nan 0.9860 0.0 0.9860
0.0523 7.0 784 0.0633 0.4947 0.9894 0.9894 nan 0.9894 0.0 0.9894
0.0577 8.0 896 0.0606 0.4904 0.9807 0.9807 nan 0.9807 0.0 0.9807
0.0528 9.0 1008 0.0596 0.4952 0.9904 0.9904 nan 0.9904 0.0 0.9904
0.0449 10.0 1120 0.0565 0.4925 0.9850 0.9850 nan 0.9850 0.0 0.9850
0.0466 11.0 1232 0.0533 0.4926 0.9853 0.9853 nan 0.9853 0.0 0.9853
0.0464 12.0 1344 0.0500 0.4937 0.9874 0.9874 nan 0.9874 0.0 0.9874
0.0456 13.0 1456 0.0503 0.4957 0.9914 0.9914 nan 0.9914 0.0 0.9914
0.0394 14.0 1568 0.0491 0.4938 0.9876 0.9876 nan 0.9876 0.0 0.9876
0.0402 15.0 1680 0.0514 0.4960 0.9921 0.9921 nan 0.9921 0.0 0.9921
0.0421 16.0 1792 0.0489 0.4955 0.9910 0.9910 nan 0.9910 0.0 0.9910
0.0453 17.0 1904 0.0461 0.4947 0.9894 0.9894 nan 0.9894 0.0 0.9894
0.0449 18.0 2016 0.0485 0.4929 0.9858 0.9858 nan 0.9858 0.0 0.9858
0.0349 19.0 2128 0.0468 0.4962 0.9925 0.9925 nan 0.9925 0.0 0.9925
0.0351 20.0 2240 0.0470 0.4962 0.9924 0.9924 nan 0.9924 0.0 0.9924
0.0324 21.0 2352 0.0452 0.4949 0.9897 0.9897 nan 0.9897 0.0 0.9897
0.0367 22.0 2464 0.0461 0.4949 0.9897 0.9897 nan 0.9897 0.0 0.9897
0.0350 23.0 2576 0.0451 0.4952 0.9903 0.9903 nan 0.9903 0.0 0.9903
0.0354 24.0 2688 0.0469 0.4957 0.9914 0.9914 nan 0.9914 0.0 0.9914
0.0353 25.0 2800 0.0452 0.4945 0.9890 0.9890 nan 0.9890 0.0 0.9890
0.0334 26.0 2912 0.0448 0.4962 0.9924 0.9924 nan 0.9924 0.0 0.9924
0.0269 27.0 3024 0.0448 0.4958 0.9915 0.9915 nan 0.9915 0.0 0.9915
0.0319 28.0 3136 0.0443 0.4949 0.9898 0.9898 nan 0.9898 0.0 0.9898
0.0293 29.0 3248 0.0450 0.4962 0.9924 0.9924 nan 0.9924 0.0 0.9924
0.0306 30.0 3360 0.0438 0.4962 0.9923 0.9923 nan 0.9923 0.0 0.9923
0.0278 31.0 3472 0.0447 0.4960 0.9920 0.9920 nan 0.9920 0.0 0.9920
0.0268 32.0 3584 0.0459 0.4962 0.9924 0.9924 nan 0.9924 0.0 0.9924
0.0269 33.0 3696 0.0434 0.4950 0.9899 0.9899 nan 0.9899 0.0 0.9899
0.0268 34.0 3808 0.0445 0.4953 0.9906 0.9906 nan 0.9906 0.0 0.9906
0.0302 35.0 3920 0.0443 0.4946 0.9891 0.9891 nan 0.9891 0.0 0.9891
0.0239 36.0 4032 0.0439 0.4959 0.9919 0.9919 nan 0.9919 0.0 0.9919
0.0268 37.0 4144 0.0442 0.4958 0.9915 0.9915 nan 0.9915 0.0 0.9915
0.0318 38.0 4256 0.0451 0.4958 0.9916 0.9916 nan 0.9916 0.0 0.9916
0.0276 39.0 4368 0.0444 0.4956 0.9912 0.9912 nan 0.9912 0.0 0.9912
0.0248 40.0 4480 0.0456 0.4960 0.9921 0.9921 nan 0.9921 0.0 0.9921
0.0244 41.0 4592 0.0449 0.4952 0.9905 0.9905 nan 0.9905 0.0 0.9905
0.0235 42.0 4704 0.0445 0.4961 0.9922 0.9922 nan 0.9922 0.0 0.9922
0.0241 43.0 4816 0.0445 0.4960 0.9920 0.9920 nan 0.9920 0.0 0.9920
0.0295 44.0 4928 0.0445 0.4959 0.9919 0.9919 nan 0.9919 0.0 0.9919
0.0252 45.0 5040 0.0443 0.4960 0.9919 0.9919 nan 0.9919 0.0 0.9919
0.0213 46.0 5152 0.0443 0.4961 0.9922 0.9922 nan 0.9922 0.0 0.9922
0.0238 47.0 5264 0.0446 0.4958 0.9917 0.9917 nan 0.9917 0.0 0.9917
0.0234 48.0 5376 0.0445 0.4959 0.9918 0.9918 nan 0.9918 0.0 0.9918
0.0223 49.0 5488 0.0445 0.4957 0.9914 0.9914 nan 0.9914 0.0 0.9914
0.0245 50.0 5600 0.0447 0.4959 0.9917 0.9917 nan 0.9917 0.0 0.9917

Framework versions

  • Transformers 5.8.0
  • Pytorch 2.11.0+cu130
  • Datasets 4.8.5
  • Tokenizers 0.22.2