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segformer-b0-finetuned-neurosymbolic-contingency-bag1-v0.1-v0

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segformer-b0-finetuned-neurosymbolic-contingency-bag1-v0.1-v0

This model is a fine-tuned version of nvidia/mit-b0 on the sam1120/neurosymbolic-contingency-bag1_v0.1 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0517
  • Mean Iou: 0.8294
  • Mean Accuracy: 0.9178
  • Overall Accuracy: 0.9912
  • Accuracy Unlabeled: nan
  • Accuracy Safe: 0.8411
  • Accuracy Unsafe: 0.9945
  • Iou Unlabeled: nan
  • Iou Safe: 0.6677
  • Iou Unsafe: 0.9911

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: 5e-05
  • 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: 0.05
  • num_epochs: 1000

Training results

Training Loss Epoch Step Validation Loss Mean Iou Mean Accuracy Overall Accuracy Accuracy Unlabeled Accuracy Safe Accuracy Unsafe Iou Unlabeled Iou Safe Iou Unsafe
1.1172 5.71 40 1.0780 0.1766 0.4855 0.4277 nan 0.5458 0.4252 0.0 0.1056 0.4241
0.9529 11.43 80 0.8894 0.3540 0.8254 0.8555 nan 0.7939 0.8568 0.0 0.2069 0.8551
0.7189 17.14 120 0.6524 0.4294 0.7983 0.9629 nan 0.6265 0.9701 0.0 0.3250 0.9632
0.5299 22.86 160 0.5716 0.4361 0.7942 0.9736 nan 0.6070 0.9815 0.0 0.3350 0.9733
0.4823 28.57 200 0.4669 0.4382 0.7262 0.9808 nan 0.4605 0.9919 0.0 0.3341 0.9806
0.3745 34.29 240 0.4167 0.6717 0.7617 0.9804 nan 0.5334 0.9900 nan 0.3632 0.9802
0.3275 40.0 280 0.3837 0.6697 0.7794 0.9788 nan 0.5713 0.9875 nan 0.3608 0.9786
0.2371 45.71 320 0.2544 0.7179 0.7764 0.9858 nan 0.5579 0.9949 nan 0.4503 0.9856
0.1794 51.43 360 0.1889 0.6812 0.7086 0.9855 nan 0.4197 0.9976 nan 0.3770 0.9854
0.1309 57.14 400 0.1450 0.7541 0.8153 0.9878 nan 0.6353 0.9953 nan 0.5205 0.9876
0.0972 62.86 440 0.1247 0.7752 0.8580 0.9883 nan 0.7221 0.9939 nan 0.5624 0.9881
0.0776 68.57 480 0.0986 0.8046 0.8875 0.9900 nan 0.7806 0.9944 nan 0.6193 0.9898
0.0614 74.29 520 0.0836 0.8129 0.8933 0.9905 nan 0.7919 0.9947 nan 0.6355 0.9903
0.0542 80.0 560 0.0859 0.8061 0.9127 0.9895 nan 0.8326 0.9928 nan 0.6228 0.9893
0.0446 85.71 600 0.0596 0.8330 0.8995 0.9919 nan 0.8031 0.9959 nan 0.6743 0.9918
0.0425 91.43 640 0.0743 0.8116 0.9024 0.9902 nan 0.8108 0.9940 nan 0.6331 0.9900
0.0361 97.14 680 0.0628 0.8230 0.9082 0.9910 nan 0.8219 0.9946 nan 0.6552 0.9908
0.0355 102.86 720 0.0568 0.8242 0.9036 0.9911 nan 0.8121 0.9950 nan 0.6574 0.9910
0.0308 108.57 760 0.0547 0.8157 0.9073 0.9904 nan 0.8205 0.9940 nan 0.6412 0.9902
0.0275 114.29 800 0.0546 0.8257 0.9103 0.9911 nan 0.8260 0.9946 nan 0.6605 0.9910
0.0279 120.0 840 0.0544 0.8211 0.9057 0.9909 nan 0.8169 0.9946 nan 0.6514 0.9907
0.0252 125.71 880 0.0518 0.8259 0.9022 0.9913 nan 0.8093 0.9952 nan 0.6607 0.9912
0.0235 131.43 920 0.0724 0.7991 0.9098 0.9889 nan 0.8272 0.9924 nan 0.6095 0.9887
0.0207 137.14 960 0.0592 0.8060 0.9040 0.9897 nan 0.8145 0.9934 nan 0.6225 0.9895
0.0196 142.86 1000 0.0602 0.8067 0.9045 0.9897 nan 0.8156 0.9934 nan 0.6239 0.9895
0.019 148.57 1040 0.0580 0.8081 0.9062 0.9898 nan 0.8190 0.9934 nan 0.6266 0.9896
0.0175 154.29 1080 0.0528 0.8106 0.9041 0.9901 nan 0.8144 0.9938 nan 0.6313 0.9899
0.02 160.0 1120 0.0617 0.8076 0.9058 0.9898 nan 0.8182 0.9934 nan 0.6257 0.9896
0.0177 165.71 1160 0.0637 0.8017 0.9088 0.9892 nan 0.8250 0.9927 nan 0.6143 0.9890
0.0172 171.43 1200 0.0587 0.8072 0.9126 0.9896 nan 0.8322 0.9929 nan 0.6250 0.9894
0.0145 177.14 1240 0.0524 0.8130 0.9137 0.9900 nan 0.8340 0.9934 nan 0.6361 0.9898
0.0141 182.86 1280 0.0526 0.8099 0.9063 0.9899 nan 0.8191 0.9936 nan 0.6300 0.9898
0.0137 188.57 1320 0.0607 0.8074 0.9183 0.9894 nan 0.8441 0.9925 nan 0.6256 0.9892
0.0134 194.29 1360 0.0497 0.8171 0.9096 0.9905 nan 0.8253 0.9940 nan 0.6440 0.9903
0.0177 200.0 1400 0.0618 0.8010 0.9055 0.9892 nan 0.8182 0.9929 nan 0.6130 0.9890
0.0126 205.71 1440 0.0564 0.8095 0.9202 0.9896 nan 0.8478 0.9926 nan 0.6297 0.9894
0.0129 211.43 1480 0.0597 0.8079 0.9013 0.9899 nan 0.8089 0.9938 nan 0.6260 0.9897
0.0301 217.14 1520 0.0507 0.8156 0.9141 0.9902 nan 0.8346 0.9936 nan 0.6412 0.9901
0.0407 222.86 1560 0.0689 0.7913 0.9207 0.9879 nan 0.8506 0.9908 nan 0.5950 0.9877
0.0107 228.57 1600 0.0420 0.8275 0.9173 0.9911 nan 0.8403 0.9943 nan 0.6641 0.9910
0.0105 234.29 1640 0.0376 0.8405 0.9172 0.9921 nan 0.8390 0.9954 nan 0.6890 0.9919
0.0115 240.0 1680 0.0634 0.7964 0.9160 0.9885 nan 0.8404 0.9917 nan 0.6046 0.9883
0.0107 245.71 1720 0.0582 0.8029 0.9213 0.9890 nan 0.8507 0.9919 nan 0.6171 0.9888
0.0098 251.43 1760 0.0550 0.8064 0.9251 0.9892 nan 0.8581 0.9920 nan 0.6238 0.9890
0.011 257.14 1800 0.0527 0.8114 0.9164 0.9898 nan 0.8398 0.9930 nan 0.6332 0.9896
0.0092 262.86 1840 0.0477 0.8195 0.9141 0.9905 nan 0.8343 0.9939 nan 0.6486 0.9904
0.0099 268.57 1880 0.0493 0.8197 0.9177 0.9905 nan 0.8418 0.9937 nan 0.6492 0.9903
0.0087 274.29 1920 0.0433 0.8287 0.9190 0.9912 nan 0.8437 0.9943 nan 0.6665 0.9910
0.0124 280.0 1960 0.0450 0.8235 0.9101 0.9910 nan 0.8257 0.9945 nan 0.6562 0.9908
0.0085 285.71 2000 0.0559 0.8077 0.9156 0.9895 nan 0.8385 0.9927 nan 0.6260 0.9893
0.0086 291.43 2040 0.0458 0.8311 0.9195 0.9913 nan 0.8445 0.9945 nan 0.6710 0.9912
0.0087 297.14 2080 0.0344 0.8496 0.9113 0.9928 nan 0.8261 0.9964 nan 0.7064 0.9927
0.0093 302.86 2120 0.0519 0.8176 0.9158 0.9904 nan 0.8379 0.9936 nan 0.6450 0.9902
0.0108 308.57 2160 0.0520 0.8126 0.9212 0.9898 nan 0.8496 0.9928 nan 0.6355 0.9896
0.0085 314.29 2200 0.0514 0.8135 0.9221 0.9899 nan 0.8515 0.9928 nan 0.6373 0.9897
0.0092 320.0 2240 0.0359 0.8461 0.9218 0.9924 nan 0.8482 0.9955 nan 0.7000 0.9923
0.0077 325.71 2280 0.0564 0.8137 0.9124 0.9901 nan 0.8313 0.9935 nan 0.6375 0.9899
0.0074 331.43 2320 0.0540 0.8176 0.9130 0.9904 nan 0.8322 0.9938 nan 0.6449 0.9903
0.0078 337.14 2360 0.0450 0.8254 0.9135 0.9910 nan 0.8326 0.9944 nan 0.6599 0.9909
0.0069 342.86 2400 0.0444 0.8277 0.9169 0.9911 nan 0.8395 0.9944 nan 0.6644 0.9910
0.0078 348.57 2440 0.0428 0.8289 0.9189 0.9912 nan 0.8435 0.9943 nan 0.6668 0.9910
0.0075 354.29 2480 0.0443 0.8294 0.9148 0.9913 nan 0.8349 0.9947 nan 0.6677 0.9912
0.0073 360.0 2520 0.0529 0.8187 0.9178 0.9904 nan 0.8420 0.9936 nan 0.6471 0.9902
0.0085 365.71 2560 0.0442 0.8274 0.9219 0.9910 nan 0.8499 0.9940 nan 0.6640 0.9908
0.0075 371.43 2600 0.0461 0.8256 0.9104 0.9911 nan 0.8261 0.9946 nan 0.6603 0.9910
0.0074 377.14 2640 0.0469 0.8262 0.9134 0.9911 nan 0.8323 0.9945 nan 0.6614 0.9909
0.0066 382.86 2680 0.0456 0.8290 0.9203 0.9912 nan 0.8464 0.9943 nan 0.6670 0.9910
0.0066 388.57 2720 0.0443 0.8321 0.9162 0.9915 nan 0.8377 0.9948 nan 0.6729 0.9913
0.0061 394.29 2760 0.0480 0.8228 0.9255 0.9906 nan 0.8575 0.9934 nan 0.6552 0.9904
0.0055 400.0 2800 0.0467 0.8271 0.9180 0.9911 nan 0.8418 0.9943 nan 0.6633 0.9909
0.0068 405.71 2840 0.0440 0.8326 0.9210 0.9914 nan 0.8475 0.9945 nan 0.6739 0.9913
0.0057 411.43 2880 0.0476 0.8253 0.9120 0.9911 nan 0.8296 0.9945 nan 0.6597 0.9909
0.0059 417.14 2920 0.0483 0.8256 0.9195 0.9909 nan 0.8450 0.9940 nan 0.6604 0.9908
0.0058 422.86 2960 0.0451 0.8308 0.9208 0.9913 nan 0.8472 0.9944 nan 0.6705 0.9911
0.0064 428.57 3000 0.0453 0.8307 0.9175 0.9914 nan 0.8405 0.9946 nan 0.6703 0.9912
0.0059 434.29 3040 0.0492 0.8228 0.9172 0.9907 nan 0.8404 0.9940 nan 0.6550 0.9906
0.0058 440.0 3080 0.0623 0.8002 0.9158 0.9889 nan 0.8396 0.9920 nan 0.6118 0.9887
0.0062 445.71 3120 0.0536 0.8184 0.9214 0.9903 nan 0.8494 0.9933 nan 0.6468 0.9901
0.0053 451.43 3160 0.0484 0.8241 0.9205 0.9908 nan 0.8472 0.9938 nan 0.6575 0.9906
0.0051 457.14 3200 0.0493 0.8239 0.9159 0.9909 nan 0.8376 0.9941 nan 0.6572 0.9907
0.0048 462.86 3240 0.0551 0.8116 0.9148 0.9899 nan 0.8365 0.9932 nan 0.6334 0.9897
0.0061 468.57 3280 0.0580 0.8104 0.9199 0.9897 nan 0.8471 0.9927 nan 0.6314 0.9895
0.0054 474.29 3320 0.0569 0.8133 0.9121 0.9901 nan 0.8307 0.9935 nan 0.6367 0.9899
0.0054 480.0 3360 0.0481 0.8273 0.9179 0.9911 nan 0.8415 0.9943 nan 0.6636 0.9909
0.0061 485.71 3400 0.0547 0.8156 0.9125 0.9903 nan 0.8314 0.9937 nan 0.6412 0.9901
0.0062 491.43 3440 0.0495 0.8234 0.9208 0.9907 nan 0.8478 0.9938 nan 0.6563 0.9906
0.0059 497.14 3480 0.0507 0.8228 0.9175 0.9907 nan 0.8411 0.9939 nan 0.6550 0.9906
0.0069 502.86 3520 0.0517 0.8210 0.9162 0.9906 nan 0.8385 0.9939 nan 0.6516 0.9905
0.0051 508.57 3560 0.0497 0.8226 0.9173 0.9907 nan 0.8406 0.9939 nan 0.6546 0.9906
0.0046 514.29 3600 0.0470 0.8278 0.9128 0.9912 nan 0.8309 0.9947 nan 0.6645 0.9911
0.0045 520.0 3640 0.0471 0.8289 0.9209 0.9911 nan 0.8477 0.9942 nan 0.6667 0.9910
0.0052 525.71 3680 0.0490 0.8251 0.9206 0.9909 nan 0.8472 0.9939 nan 0.6596 0.9907
0.0064 531.43 3720 0.0522 0.8200 0.9129 0.9906 nan 0.8318 0.9940 nan 0.6495 0.9904
0.0054 537.14 3760 0.0499 0.8229 0.9175 0.9907 nan 0.8410 0.9939 nan 0.6552 0.9906
0.0047 542.86 3800 0.0523 0.8196 0.9220 0.9904 nan 0.8506 0.9934 nan 0.6490 0.9902
0.0053 548.57 3840 0.0487 0.8265 0.9155 0.9911 nan 0.8367 0.9944 nan 0.6620 0.9909
0.0053 554.29 3880 0.0527 0.8210 0.9174 0.9906 nan 0.8409 0.9938 nan 0.6515 0.9904
0.0051 560.0 3920 0.0478 0.8299 0.9186 0.9913 nan 0.8427 0.9944 nan 0.6687 0.9911
0.006 565.71 3960 0.0423 0.8369 0.9174 0.9918 nan 0.8397 0.9951 nan 0.6822 0.9917
0.0048 571.43 4000 0.0534 0.8190 0.9176 0.9904 nan 0.8416 0.9936 nan 0.6477 0.9903
0.0051 577.14 4040 0.0513 0.8222 0.9211 0.9906 nan 0.8485 0.9936 nan 0.6539 0.9904
0.0046 582.86 4080 0.0435 0.8378 0.9182 0.9919 nan 0.8413 0.9951 nan 0.6838 0.9917
0.0051 588.57 4120 0.0481 0.8285 0.9215 0.9911 nan 0.8489 0.9941 nan 0.6661 0.9909
0.0061 594.29 4160 0.0530 0.8213 0.9202 0.9906 nan 0.8468 0.9936 nan 0.6521 0.9904
0.0062 600.0 4200 0.0531 0.8199 0.9190 0.9905 nan 0.8444 0.9936 nan 0.6496 0.9903
0.0047 605.71 4240 0.0519 0.8231 0.9183 0.9907 nan 0.8426 0.9939 nan 0.6557 0.9906
0.0044 611.43 4280 0.0503 0.8247 0.9183 0.9909 nan 0.8425 0.9940 nan 0.6587 0.9907
0.0047 617.14 4320 0.0569 0.8184 0.9169 0.9904 nan 0.8401 0.9936 nan 0.6465 0.9902
0.0047 622.86 4360 0.0540 0.8193 0.9195 0.9904 nan 0.8455 0.9935 nan 0.6483 0.9902
0.0051 628.57 4400 0.0519 0.8231 0.9129 0.9909 nan 0.8315 0.9943 nan 0.6556 0.9907
0.0041 634.29 4440 0.0562 0.8143 0.9212 0.9900 nan 0.8494 0.9930 nan 0.6388 0.9898
0.0049 640.0 4480 0.0541 0.8191 0.9139 0.9905 nan 0.8339 0.9939 nan 0.6479 0.9904
0.0042 645.71 4520 0.0534 0.8204 0.9203 0.9905 nan 0.8471 0.9935 nan 0.6505 0.9903
0.0044 651.43 4560 0.0537 0.8211 0.9184 0.9906 nan 0.8431 0.9937 nan 0.6518 0.9904
0.0038 657.14 4600 0.0530 0.8220 0.9204 0.9906 nan 0.8471 0.9937 nan 0.6536 0.9904
0.0044 662.86 4640 0.0493 0.8286 0.9194 0.9911 nan 0.8446 0.9943 nan 0.6662 0.9910
0.0045 668.57 4680 0.0590 0.8150 0.9143 0.9902 nan 0.8350 0.9935 nan 0.6399 0.9900
0.0047 674.29 4720 0.0560 0.8188 0.9204 0.9903 nan 0.8474 0.9934 nan 0.6474 0.9902
0.0042 680.0 4760 0.0504 0.8271 0.9201 0.9910 nan 0.8461 0.9941 nan 0.6634 0.9909
0.0045 685.71 4800 0.0442 0.8298 0.9084 0.9915 nan 0.8218 0.9951 nan 0.6683 0.9913
0.0039 691.43 4840 0.0442 0.8341 0.9137 0.9917 nan 0.8324 0.9951 nan 0.6768 0.9915
0.0043 697.14 4880 0.0483 0.8306 0.9173 0.9913 nan 0.8401 0.9946 nan 0.6700 0.9912
0.0038 702.86 4920 0.0482 0.8306 0.9155 0.9914 nan 0.8364 0.9947 nan 0.6699 0.9912
0.0041 708.57 4960 0.0514 0.8252 0.9159 0.9910 nan 0.8376 0.9942 nan 0.6597 0.9908
0.004 714.29 5000 0.0457 0.8334 0.9157 0.9916 nan 0.8365 0.9949 nan 0.6754 0.9914
0.0049 720.0 5040 0.0443 0.8361 0.9162 0.9918 nan 0.8374 0.9951 nan 0.6806 0.9916
0.0043 725.71 5080 0.0557 0.8195 0.9178 0.9905 nan 0.8419 0.9936 nan 0.6487 0.9903
0.0038 731.43 5120 0.0489 0.8288 0.9190 0.9912 nan 0.8436 0.9943 nan 0.6665 0.9910
0.0045 737.14 5160 0.0551 0.8200 0.9163 0.9905 nan 0.8388 0.9938 nan 0.6496 0.9904
0.0043 742.86 5200 0.0518 0.8248 0.9180 0.9909 nan 0.8419 0.9941 nan 0.6589 0.9907
0.0047 748.57 5240 0.0531 0.8227 0.9197 0.9907 nan 0.8455 0.9938 nan 0.6548 0.9905
0.0039 754.29 5280 0.0506 0.8264 0.9172 0.9910 nan 0.8401 0.9943 nan 0.6619 0.9909
0.0041 760.0 5320 0.0526 0.8237 0.9172 0.9908 nan 0.8404 0.9940 nan 0.6568 0.9907
0.0041 765.71 5360 0.0530 0.8236 0.9155 0.9908 nan 0.8368 0.9941 nan 0.6566 0.9907
0.0042 771.43 5400 0.0558 0.8208 0.9141 0.9907 nan 0.8341 0.9940 nan 0.6512 0.9905
0.0036 777.14 5440 0.0496 0.8305 0.9177 0.9913 nan 0.8409 0.9945 nan 0.6697 0.9912
0.0038 782.86 5480 0.0507 0.8287 0.9165 0.9912 nan 0.8386 0.9945 nan 0.6662 0.9911
0.0044 788.57 5520 0.0502 0.8282 0.9151 0.9912 nan 0.8356 0.9945 nan 0.6654 0.9911
0.0038 794.29 5560 0.0705 0.7906 0.9225 0.9878 nan 0.8544 0.9906 nan 0.5936 0.9875
0.0041 800.0 5600 0.0515 0.8253 0.9197 0.9909 nan 0.8454 0.9940 nan 0.6599 0.9907
0.0038 805.71 5640 0.0491 0.8318 0.9169 0.9914 nan 0.8392 0.9947 nan 0.6723 0.9913
0.0039 811.43 5680 0.0476 0.8345 0.9196 0.9916 nan 0.8445 0.9947 nan 0.6776 0.9914
0.004 817.14 5720 0.0500 0.8308 0.9212 0.9913 nan 0.8480 0.9943 nan 0.6704 0.9911
0.0039 822.86 5760 0.0504 0.8306 0.9182 0.9913 nan 0.8420 0.9945 nan 0.6700 0.9912
0.0039 828.57 5800 0.0507 0.8296 0.9186 0.9912 nan 0.8427 0.9944 nan 0.6681 0.9911
0.0034 834.29 5840 0.0490 0.8328 0.9180 0.9915 nan 0.8414 0.9947 nan 0.6743 0.9914
0.0037 840.0 5880 0.0512 0.8299 0.9199 0.9912 nan 0.8455 0.9944 nan 0.6688 0.9911
0.0038 845.71 5920 0.0474 0.8355 0.9186 0.9917 nan 0.8422 0.9949 nan 0.6794 0.9915
0.0036 851.43 5960 0.0492 0.8319 0.9189 0.9914 nan 0.8433 0.9946 nan 0.6726 0.9913
0.0034 857.14 6000 0.0569 0.8213 0.9199 0.9906 nan 0.8461 0.9937 nan 0.6522 0.9904
0.0042 862.86 6040 0.0551 0.8234 0.9187 0.9908 nan 0.8435 0.9939 nan 0.6563 0.9906
0.0041 868.57 6080 0.0558 0.8225 0.9179 0.9907 nan 0.8419 0.9939 nan 0.6544 0.9905
0.004 874.29 6120 0.0540 0.8261 0.9180 0.9910 nan 0.8419 0.9942 nan 0.6614 0.9908
0.0034 880.0 6160 0.0527 0.8277 0.9185 0.9911 nan 0.8426 0.9943 nan 0.6645 0.9909
0.0039 885.71 6200 0.0525 0.8277 0.9177 0.9911 nan 0.8411 0.9943 nan 0.6644 0.9910
0.0037 891.43 6240 0.0519 0.8281 0.9193 0.9911 nan 0.8443 0.9943 nan 0.6653 0.9910
0.0036 897.14 6280 0.0516 0.8289 0.9169 0.9912 nan 0.8393 0.9945 nan 0.6667 0.9911
0.0039 902.86 6320 0.0524 0.8283 0.9193 0.9911 nan 0.8443 0.9943 nan 0.6656 0.9910
0.0033 908.57 6360 0.0514 0.8286 0.9181 0.9912 nan 0.8418 0.9944 nan 0.6662 0.9910
0.0036 914.29 6400 0.0533 0.8277 0.9199 0.9911 nan 0.8455 0.9942 nan 0.6644 0.9909
0.0042 920.0 6440 0.0522 0.8291 0.9177 0.9912 nan 0.8410 0.9944 nan 0.6671 0.9911
0.0037 925.71 6480 0.0508 0.8312 0.9183 0.9914 nan 0.8420 0.9946 nan 0.6713 0.9912
0.0037 931.43 6520 0.0510 0.8302 0.9166 0.9913 nan 0.8386 0.9946 nan 0.6692 0.9912
0.0032 937.14 6560 0.0521 0.8296 0.9178 0.9913 nan 0.8411 0.9945 nan 0.6682 0.9911
0.0036 942.86 6600 0.0517 0.8303 0.9184 0.9913 nan 0.8422 0.9945 nan 0.6694 0.9911
0.0034 948.57 6640 0.0525 0.8295 0.9187 0.9912 nan 0.8429 0.9944 nan 0.6678 0.9911
0.0035 954.29 6680 0.0519 0.8287 0.9189 0.9912 nan 0.8434 0.9943 nan 0.6664 0.9910
0.0034 960.0 6720 0.0518 0.8305 0.9181 0.9913 nan 0.8416 0.9945 nan 0.6699 0.9912
0.0036 965.71 6760 0.0517 0.8299 0.9179 0.9913 nan 0.8414 0.9945 nan 0.6687 0.9911
0.0041 971.43 6800 0.0514 0.8298 0.9171 0.9913 nan 0.8397 0.9945 nan 0.6684 0.9911
0.004 977.14 6840 0.0529 0.8290 0.9190 0.9912 nan 0.8437 0.9943 nan 0.6670 0.9910
0.004 982.86 6880 0.0523 0.8293 0.9174 0.9913 nan 0.8404 0.9945 nan 0.6676 0.9911
0.0035 988.57 6920 0.0523 0.8290 0.9180 0.9912 nan 0.8416 0.9944 nan 0.6670 0.9911
0.0044 994.29 6960 0.0501 0.8299 0.9163 0.9913 nan 0.8380 0.9946 nan 0.6687 0.9912
0.0036 1000.0 7000 0.0517 0.8294 0.9178 0.9912 nan 0.8411 0.9945 nan 0.6677 0.9911

Framework versions

  • Transformers 4.30.2
  • Pytorch 2.0.1+cu117
  • Datasets 2.13.1
  • Tokenizers 0.13.3
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Safetensors
Model size
3.72M params
Tensor type
F32
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Hosted inference API
Image Segmentation
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Evaluation results

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