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JCAI2000
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segformer-b5-finetuned-100by100PNG-50epochs-attempt2-100epochs-backgroundclass-2

Image Segmentation
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PyTorch
segformer
vision
Generated from Trainer
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segformer-b5-finetuned-100by100PNG-50epochs-attempt2-100epochs-backgroundclass-2 / README.md
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metadata
license: other
base_model: nvidia/mit-b5
tags:
  - vision
  - image-segmentation
  - generated_from_trainer
model-index:
  - name: >-
      segformer-b3-finetuned-100by100PNG-50epochs-attempt2-100epochs-backgroundclass
    results: []

segformer-b3-finetuned-100by100PNG-50epochs-attempt2-100epochs-backgroundclass

This model is a fine-tuned version of nvidia/mit-b5 on the JCAI2000/100By100BranchPNG dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1497
  • Mean Iou: 0.8933
  • Mean Accuracy: 0.9531
  • Overall Accuracy: 0.9662
  • Accuracy Background: 0.9732
  • Accuracy Branch: 0.9330
  • Iou Background: 0.9597
  • Iou Branch: 0.8270

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: 2
  • eval_batch_size: 2
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 100

Training results

Training Loss Epoch Step Validation Loss Mean Iou Mean Accuracy Overall Accuracy Accuracy Background Accuracy Branch Iou Background Iou Branch
0.2055 1.05 20 0.2925 0.8151 0.9469 0.9320 0.9242 0.9695 0.9183 0.7118
0.1549 2.11 40 0.1328 0.8802 0.9311 0.9628 0.9796 0.8825 0.9561 0.8043
0.0735 3.16 60 0.1178 0.8804 0.9512 0.9613 0.9666 0.9357 0.9538 0.8070
0.0636 4.21 80 0.0844 0.8966 0.9368 0.9686 0.9854 0.8881 0.9629 0.8303
0.0546 5.26 100 0.1099 0.8969 0.9526 0.9676 0.9756 0.9297 0.9614 0.8325
0.0567 6.32 120 0.1012 0.8996 0.9500 0.9688 0.9788 0.9213 0.9629 0.8364
0.0515 7.37 140 0.1137 0.8935 0.9462 0.9668 0.9777 0.9147 0.9605 0.8265
0.052 8.42 160 0.0987 0.8914 0.9317 0.9670 0.9858 0.8776 0.9611 0.8217
0.0358 9.47 180 0.1167 0.8978 0.9581 0.9676 0.9726 0.9435 0.9613 0.8344
0.0254 10.53 200 0.0767 0.9111 0.9519 0.9729 0.9840 0.9197 0.9678 0.8545
0.0483 11.58 220 0.0953 0.9037 0.9524 0.9701 0.9795 0.9253 0.9645 0.8429
0.0285 12.63 240 0.0904 0.9026 0.9490 0.9700 0.9811 0.9169 0.9643 0.8409
0.0389 13.68 260 0.0902 0.9025 0.9472 0.9701 0.9821 0.9123 0.9644 0.8406
0.0473 14.74 280 0.0852 0.9084 0.9522 0.9719 0.9823 0.9220 0.9665 0.8502
0.0266 15.79 300 0.0983 0.8985 0.9409 0.9690 0.9839 0.8979 0.9633 0.8337
0.0233 16.84 320 0.0965 0.9052 0.9601 0.9702 0.9756 0.9447 0.9644 0.8460
0.0257 17.89 340 0.0941 0.9039 0.9550 0.9701 0.9781 0.9319 0.9643 0.8434
0.0352 18.95 360 0.0855 0.9043 0.9483 0.9706 0.9824 0.9142 0.9651 0.8435
0.1941 20.0 380 0.0946 0.9045 0.9509 0.9706 0.9809 0.9210 0.9650 0.8441
0.0325 21.05 400 0.0972 0.8973 0.9449 0.9683 0.9807 0.9092 0.9624 0.8323
0.0159 22.11 420 0.0828 0.9081 0.9528 0.9717 0.9817 0.9239 0.9664 0.8498
0.0175 23.16 440 0.1061 0.8995 0.9491 0.9688 0.9793 0.9188 0.9629 0.8360
0.0281 24.21 460 0.1090 0.8969 0.9516 0.9677 0.9761 0.9271 0.9615 0.8323
0.0177 25.26 480 0.1122 0.8983 0.9547 0.9680 0.9750 0.9343 0.9618 0.8347
0.0228 26.32 500 0.1088 0.8957 0.9546 0.9670 0.9736 0.9357 0.9606 0.8307
0.0348 27.37 520 0.0933 0.9059 0.9524 0.9710 0.9808 0.9241 0.9654 0.8464
0.0177 28.42 540 0.1053 0.9025 0.9527 0.9697 0.9787 0.9268 0.9639 0.8411
0.0182 29.47 560 0.1039 0.8992 0.9473 0.9688 0.9802 0.9143 0.9630 0.8355
0.0171 30.53 580 0.1117 0.8991 0.9555 0.9682 0.9750 0.9360 0.9621 0.8361
0.0275 31.58 600 0.1142 0.8935 0.9497 0.9665 0.9754 0.9241 0.9601 0.8268
0.0186 32.63 620 0.1065 0.9024 0.9524 0.9697 0.9788 0.9261 0.9639 0.8408
0.0173 33.68 640 0.1081 0.8986 0.9529 0.9682 0.9764 0.9294 0.9621 0.8351
0.015 34.74 660 0.1243 0.8935 0.9530 0.9663 0.9733 0.9327 0.9598 0.8272
0.0183 35.79 680 0.1120 0.9005 0.9500 0.9691 0.9792 0.9209 0.9633 0.8377
0.0248 36.84 700 0.1185 0.8962 0.9517 0.9674 0.9757 0.9277 0.9611 0.8312
0.0104 37.89 720 0.1136 0.8975 0.9506 0.9680 0.9771 0.9241 0.9619 0.8332
0.0481 38.95 740 0.1127 0.9010 0.9528 0.9691 0.9778 0.9277 0.9632 0.8388
0.0153 40.0 760 0.1101 0.9019 0.9537 0.9694 0.9777 0.9297 0.9635 0.8402
0.0143 41.05 780 0.1105 0.9032 0.9558 0.9698 0.9771 0.9345 0.9639 0.8425
0.0104 42.11 800 0.1122 0.8986 0.9428 0.9689 0.9827 0.9028 0.9631 0.8340
0.0172 43.16 820 0.1097 0.9041 0.9540 0.9702 0.9788 0.9291 0.9645 0.8437
0.0371 44.21 840 0.1064 0.9011 0.9503 0.9693 0.9794 0.9212 0.9635 0.8387
0.0221 45.26 860 0.1150 0.9004 0.9515 0.9690 0.9783 0.9247 0.9631 0.8377
0.0186 46.32 880 0.1228 0.8958 0.9518 0.9672 0.9754 0.9282 0.9610 0.8306
0.0119 47.37 900 0.1205 0.8980 0.9525 0.9680 0.9762 0.9288 0.9619 0.8340
0.0113 48.42 920 0.1133 0.8998 0.9502 0.9688 0.9787 0.9216 0.9629 0.8366
0.0121 49.47 940 0.1145 0.8993 0.9490 0.9688 0.9792 0.9188 0.9629 0.8358
0.0263 50.53 960 0.1168 0.8977 0.9542 0.9678 0.9750 0.9334 0.9616 0.8338
0.0093 51.58 980 0.1213 0.8940 0.9534 0.9664 0.9733 0.9334 0.9600 0.8280
0.0193 52.63 1000 0.1241 0.8971 0.9507 0.9678 0.9769 0.9246 0.9617 0.8326
0.0139 53.68 1020 0.1263 0.8962 0.9546 0.9672 0.9739 0.9353 0.9609 0.8316
0.012 54.74 1040 0.1252 0.8952 0.9504 0.9671 0.9760 0.9247 0.9609 0.8296
0.008 55.79 1060 0.1219 0.8986 0.9516 0.9683 0.9772 0.9260 0.9623 0.8349
0.0092 56.84 1080 0.1290 0.8995 0.9552 0.9684 0.9754 0.9349 0.9623 0.8366
0.015 57.89 1100 0.1243 0.8989 0.9545 0.9682 0.9755 0.9335 0.9621 0.8358
0.0126 58.95 1120 0.1214 0.8977 0.9541 0.9678 0.9751 0.9331 0.9616 0.8337
0.0212 60.0 1140 0.1298 0.8953 0.9542 0.9669 0.9736 0.9347 0.9605 0.8301
0.0192 61.05 1160 0.1341 0.8930 0.9518 0.9661 0.9737 0.9299 0.9597 0.8262
0.0136 62.11 1180 0.1327 0.8970 0.9528 0.9676 0.9754 0.9302 0.9614 0.8325
0.0131 63.16 1200 0.1233 0.8997 0.9549 0.9685 0.9757 0.9340 0.9624 0.8369
0.0135 64.21 1220 0.1301 0.8957 0.9542 0.9670 0.9738 0.9345 0.9607 0.8307
0.0228 65.26 1240 0.1274 0.8979 0.9524 0.9680 0.9762 0.9285 0.9618 0.8339
0.0138 66.32 1260 0.1336 0.8965 0.9520 0.9675 0.9757 0.9283 0.9613 0.8318
0.0127 67.37 1280 0.1278 0.8980 0.9519 0.9681 0.9767 0.9271 0.9620 0.8341
0.0107 68.42 1300 0.1293 0.8970 0.9530 0.9676 0.9753 0.9308 0.9614 0.8327
0.0278 69.47 1320 0.1413 0.8926 0.9534 0.9659 0.9725 0.9343 0.9593 0.8258
0.0159 70.53 1340 0.1360 0.8953 0.9522 0.9670 0.9748 0.9296 0.9607 0.8298
0.0105 71.58 1360 0.1319 0.8972 0.9537 0.9676 0.9750 0.9324 0.9614 0.8330
0.0168 72.63 1380 0.1343 0.8942 0.9533 0.9665 0.9735 0.9331 0.9601 0.8283
0.0156 73.68 1400 0.1357 0.8950 0.9516 0.9669 0.9751 0.9281 0.9606 0.8294
0.0109 74.74 1420 0.1446 0.8905 0.9524 0.9652 0.9719 0.9328 0.9585 0.8226
0.0168 75.79 1440 0.1339 0.8958 0.9533 0.9671 0.9745 0.9320 0.9608 0.8308
0.0252 76.84 1460 0.1355 0.8935 0.9532 0.9662 0.9731 0.9333 0.9597 0.8272
0.0109 77.89 1480 0.1388 0.8932 0.9533 0.9661 0.9729 0.9338 0.9596 0.8267
0.0109 78.95 1500 0.1404 0.8924 0.9519 0.9659 0.9734 0.9305 0.9594 0.8255
0.0112 80.0 1520 0.1424 0.8921 0.9535 0.9657 0.9722 0.9349 0.9591 0.8251
0.0094 81.05 1540 0.1451 0.8924 0.9524 0.9659 0.9730 0.9317 0.9593 0.8254
0.007 82.11 1560 0.1457 0.8931 0.9527 0.9661 0.9732 0.9322 0.9596 0.8266
0.0119 83.16 1580 0.1424 0.8927 0.9520 0.9661 0.9735 0.9306 0.9595 0.8259
0.0153 84.21 1600 0.1535 0.8909 0.9530 0.9653 0.9718 0.9342 0.9586 0.8233
0.0104 85.26 1620 0.1452 0.8921 0.9529 0.9658 0.9725 0.9333 0.9592 0.8251
0.0101 86.32 1640 0.1503 0.8910 0.9536 0.9653 0.9714 0.9358 0.9586 0.8235
0.009 87.37 1660 0.1508 0.8925 0.9532 0.9659 0.9725 0.9339 0.9593 0.8257
0.0073 88.42 1680 0.1419 0.8949 0.9528 0.9668 0.9742 0.9315 0.9604 0.8293
0.0137 89.47 1700 0.1437 0.8942 0.9526 0.9666 0.9739 0.9313 0.9601 0.8282
0.0061 90.53 1720 0.1474 0.8928 0.9523 0.9660 0.9733 0.9313 0.9595 0.8260
0.0132 91.58 1740 0.1408 0.8935 0.9522 0.9663 0.9738 0.9306 0.9598 0.8271
0.0089 92.63 1760 0.1468 0.8933 0.9527 0.9662 0.9734 0.9320 0.9597 0.8268
0.0141 93.68 1780 0.1458 0.8930 0.9529 0.9661 0.9731 0.9328 0.9596 0.8265
0.0132 94.74 1800 0.1442 0.8934 0.9528 0.9662 0.9733 0.9324 0.9597 0.8270
0.0109 95.79 1820 0.1445 0.8928 0.9529 0.9660 0.9730 0.9327 0.9595 0.8262
0.0248 96.84 1840 0.1391 0.8938 0.9529 0.9664 0.9736 0.9322 0.9599 0.8276
0.0074 97.89 1860 0.1424 0.8938 0.9531 0.9664 0.9735 0.9327 0.9599 0.8277
0.0072 98.95 1880 0.1465 0.8931 0.9534 0.9661 0.9728 0.9339 0.9596 0.8266
0.0183 100.0 1900 0.1497 0.8933 0.9531 0.9662 0.9732 0.9330 0.9597 0.8270

Framework versions

  • Transformers 4.33.0
  • Pytorch 2.0.1+cu117
  • Datasets 2.14.4
  • Tokenizers 0.13.3