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rwood-97
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test_os_counties

Image Segmentation
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PyTorch
segformer
vision
Generated from Trainer
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test_os_counties / README.md
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metadata
license: other
base_model: nvidia/mit-b0
tags:
  - vision
  - image-segmentation
  - generated_from_trainer
model-index:
  - name: test_os_counties
    results: []

test_os_counties

This model is a fine-tuned version of nvidia/mit-b0 on the rwood-97/os_counties dataset. It achieves the following results on the evaluation set:

  • Loss: 1.8997
  • Mean Iou: 0.1075
  • Mean Accuracy: 0.4992
  • Overall Accuracy: 0.2118
  • Accuracy Non-map: 0.9899
  • Accuracy Map: 0.0084
  • Iou Non-map: 0.2065
  • Iou Map: 0.0084

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: 4
  • eval_batch_size: 4
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10

Training results

Training Loss Epoch Step Validation Loss Mean Iou Mean Accuracy Overall Accuracy Accuracy Non-map Accuracy Map Iou Non-map Iou Map
0.4081 0.41 20 0.8267 0.2101 0.4981 0.3478 0.7548 0.2414 0.1934 0.2268
0.3939 0.82 40 0.8190 0.2387 0.4978 0.3898 0.6821 0.3134 0.1881 0.2894
0.3319 1.22 60 0.9487 0.1579 0.4976 0.2759 0.8762 0.1191 0.2005 0.1153
0.3234 1.63 80 0.9275 0.1847 0.4974 0.3120 0.8138 0.1809 0.1969 0.1725
0.3932 2.04 100 0.9053 0.1892 0.4974 0.3183 0.8031 0.1916 0.1962 0.1822
0.3412 2.45 120 0.8265 0.2385 0.4973 0.3895 0.6814 0.3132 0.1878 0.2891
0.2976 2.86 140 1.3713 0.1127 0.4987 0.2182 0.9778 0.0197 0.2058 0.0195
0.2803 3.27 160 1.6436 0.1095 0.4993 0.2143 0.9860 0.0126 0.2064 0.0125
0.3686 3.67 180 1.2379 0.1221 0.4982 0.2298 0.9564 0.0399 0.2047 0.0395
0.3434 4.08 200 1.1857 0.1385 0.4978 0.2507 0.9197 0.0758 0.2028 0.0743
0.2951 4.49 220 1.3947 0.1160 0.4986 0.2223 0.9704 0.0268 0.2054 0.0266
0.2333 4.9 240 1.5480 0.1170 0.4988 0.2236 0.9687 0.0288 0.2054 0.0286
0.2491 5.31 260 1.6563 0.1136 0.4990 0.2194 0.9764 0.0215 0.2058 0.0214
0.2706 5.71 280 1.9766 0.1058 0.4995 0.2098 0.9942 0.0048 0.2068 0.0048
0.2171 6.12 300 1.6191 0.1117 0.4989 0.2170 0.9804 0.0175 0.2060 0.0174
0.2352 6.53 320 1.8075 0.1102 0.4992 0.2151 0.9842 0.0141 0.2062 0.0141
0.2953 6.94 340 1.4709 0.1178 0.4986 0.2245 0.9667 0.0305 0.2053 0.0303
0.2799 7.35 360 1.3843 0.1300 0.4982 0.2399 0.9392 0.0571 0.2038 0.0562
0.2285 7.76 380 1.7799 0.1101 0.4991 0.2150 0.9842 0.0140 0.2062 0.0139
0.3461 8.16 400 1.7282 0.1106 0.4989 0.2157 0.9826 0.0153 0.2061 0.0152
0.1867 8.57 420 2.3356 0.1042 0.4997 0.2079 0.9981 0.0014 0.2070 0.0014
0.1731 8.98 440 2.1465 0.1050 0.4996 0.2089 0.9959 0.0032 0.2069 0.0032
0.224 9.39 460 2.3467 0.1047 0.4996 0.2084 0.9969 0.0024 0.2070 0.0024
0.2199 9.8 480 1.8997 0.1075 0.4992 0.2118 0.9899 0.0084 0.2065 0.0084

Framework versions

  • Transformers 4.35.0.dev0
  • Pytorch 2.1.0
  • Datasets 2.14.5
  • Tokenizers 0.14.1