Hugging Face's logo

yolo12138
/
segformer-b2-cloth-parse-9

Image Segmentation
Transformers
TensorBoard
Safetensors
segformer
generated_from_trainer
Inference Endpoints
Model card Files Metrics Community
segformer-b2-cloth-parse-9 / README.md
yolo12138's picture
yolo12138
Update README.md
baa437e
preview code
|
raw history blame contribute delete
No virus
23.9 kB
metadata
license: mit
base_model: mattmdjaga/segformer_b2_clothes
tags:
  - generated_from_trainer
datasets:
  - cloth_parsing_mix
model-index:
  - name: segformer-b2-cloth-parse-9
    results: []
pipeline_tag: image-segmentation

segformer-b2-cloth-parse-9

This model is a fine-tuned version of mattmdjaga/segformer_b2_clothes on the cloth_parsing_mix dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0433
  • Mean Iou: 0.8611
  • Mean Accuracy: 0.9107
  • Overall Accuracy: 0.9846
  • Accuracy Background: 0.9964
  • Accuracy Upper Torso: 0.9857
  • Accuracy Left Pants: 0.9654
  • Accuracy Right Patns: 0.9664
  • Accuracy Skirts: 0.9065
  • Accuracy Left Sleeve: 0.9591
  • Accuracy Right Sleeve: 0.9662
  • Accuracy Outer Collar: 0.6491
  • Accuracy Inner Collar: 0.8015
  • Iou Background: 0.9923
  • Iou Upper Torso: 0.9655
  • Iou Left Pants: 0.9017
  • Iou Right Patns: 0.9085
  • Iou Skirts: 0.8749
  • Iou Left Sleeve: 0.9223
  • Iou Right Sleeve: 0.9289
  • Iou Outer Collar: 0.5394
  • Iou Inner Collar: 0.7160

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: 1e-05
  • train_batch_size: 12
  • eval_batch_size: 12
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 5

Training results

Training Loss Epoch Step Validation Loss Mean Iou Mean Accuracy Overall Accuracy Accuracy Background Accuracy Upper Torso Accuracy Left Pants Accuracy Right Patns Accuracy Skirts Accuracy Left Sleeve Accuracy Right Sleeve Accuracy Outer Collar Accuracy Inner Collar Iou Background Iou Upper Torso Iou Left Pants Iou Right Patns Iou Skirts Iou Left Sleeve Iou Right Sleeve Iou Outer Collar Iou Inner Collar
0.1054 0.11 500 0.1180 0.7305 0.7971 0.9670 0.9902 0.9720 0.9654 0.9756 0.8036 0.9226 0.9289 0.0716 0.5444 0.9830 0.9234 0.8752 0.8765 0.7370 0.8236 0.8232 0.0703 0.4628
0.1033 0.22 1000 0.0851 0.7862 0.8418 0.9746 0.9924 0.9829 0.9665 0.9653 0.8491 0.9145 0.9226 0.3219 0.6608 0.9866 0.9424 0.8858 0.8875 0.8105 0.8538 0.8614 0.2833 0.5642
0.0944 0.32 1500 0.0713 0.8077 0.8595 0.9773 0.9941 0.9833 0.9566 0.9625 0.8924 0.9094 0.9181 0.4414 0.6774 0.9880 0.9481 0.8937 0.8950 0.8437 0.8668 0.8751 0.3629 0.5958
0.0746 0.43 2000 0.0683 0.8190 0.8770 0.9783 0.9941 0.9796 0.9652 0.9722 0.8656 0.9480 0.9562 0.4882 0.7236 0.9888 0.9497 0.9070 0.9127 0.8306 0.8790 0.8870 0.3945 0.6218
0.0548 0.54 2500 0.0666 0.8187 0.8713 0.9787 0.9951 0.9831 0.9580 0.9606 0.8651 0.9215 0.9453 0.4839 0.7293 0.9893 0.9514 0.8939 0.9006 0.8245 0.8812 0.8964 0.4010 0.6298
0.0728 0.65 3000 0.0591 0.8271 0.8806 0.9804 0.9945 0.9839 0.9624 0.9659 0.8982 0.9399 0.9430 0.4884 0.7493 0.9900 0.9551 0.8940 0.8966 0.8583 0.8930 0.9011 0.4100 0.6458
0.0505 0.75 3500 0.0648 0.8218 0.8745 0.9797 0.9947 0.9847 0.9858 0.9905 0.8402 0.9500 0.9587 0.4480 0.7178 0.9900 0.9534 0.9022 0.9037 0.8223 0.8944 0.9017 0.3881 0.6402
0.0601 0.86 4000 0.0568 0.8415 0.8951 0.9817 0.9952 0.9817 0.9632 0.9640 0.9170 0.9521 0.9541 0.5781 0.7508 0.9903 0.9576 0.9138 0.9199 0.8716 0.9010 0.9106 0.4562 0.6529
0.0438 0.97 4500 0.0569 0.8431 0.8925 0.9815 0.9947 0.9844 0.9764 0.9838 0.8870 0.9492 0.9595 0.5561 0.7416 0.9903 0.9560 0.9287 0.9370 0.8585 0.9000 0.9089 0.4524 0.6559
0.0617 1.08 5000 0.0529 0.8417 0.8933 0.9816 0.9952 0.9841 0.9602 0.9631 0.8922 0.9475 0.9533 0.5797 0.7642 0.9907 0.9571 0.9097 0.9126 0.8488 0.9044 0.9158 0.4687 0.6678
0.0452 1.19 5500 0.0557 0.8351 0.8935 0.9812 0.9949 0.9842 0.9644 0.9667 0.8781 0.9494 0.9604 0.5961 0.7471 0.9906 0.9588 0.8803 0.8885 0.8349 0.9069 0.9169 0.4743 0.6645
0.0571 1.29 6000 0.0551 0.8351 0.8934 0.9810 0.9957 0.9831 0.9652 0.9693 0.8562 0.9593 0.9569 0.5959 0.7586 0.9910 0.9579 0.8842 0.8879 0.8188 0.9084 0.9155 0.4774 0.6749
0.0778 1.4 6500 0.0537 0.8430 0.8994 0.9818 0.9948 0.9839 0.9872 0.9921 0.8702 0.9587 0.9635 0.5790 0.7656 0.9911 0.9579 0.9044 0.9093 0.8458 0.9060 0.9157 0.4760 0.6808
0.0392 1.51 7000 0.0491 0.8503 0.9069 0.9830 0.9954 0.9823 0.9645 0.9666 0.9205 0.9534 0.9599 0.6214 0.7984 0.9916 0.9607 0.9123 0.9139 0.8755 0.9072 0.9180 0.4907 0.6830
0.0376 1.62 7500 0.0514 0.8442 0.9010 0.9819 0.9954 0.9832 0.9652 0.9660 0.8850 0.9525 0.9598 0.6257 0.7762 0.9914 0.9586 0.8944 0.9053 0.8355 0.9104 0.9215 0.4965 0.6838
0.0391 1.73 8000 0.0492 0.8422 0.8993 0.9819 0.9958 0.9836 0.9641 0.9671 0.8692 0.9561 0.9661 0.6159 0.7756 0.9916 0.9596 0.8882 0.8930 0.8338 0.9103 0.9189 0.4982 0.6860
0.0446 1.83 8500 0.0491 0.8515 0.9079 0.9829 0.9960 0.9836 0.9890 0.9913 0.8770 0.9505 0.9631 0.6458 0.7751 0.9916 0.9603 0.9114 0.9161 0.8559 0.9100 0.9217 0.5096 0.6867
0.041 1.94 9000 0.0482 0.8464 0.8978 0.9825 0.9958 0.9848 0.9619 0.9668 0.8822 0.9569 0.9659 0.5961 0.7703 0.9916 0.9602 0.8958 0.9018 0.8438 0.9148 0.9231 0.4966 0.6899
0.0744 2.05 9500 0.0474 0.8523 0.9018 0.9834 0.9961 0.9840 0.9598 0.9633 0.9195 0.9471 0.9644 0.6055 0.7766 0.9919 0.9619 0.9095 0.9125 0.8697 0.9113 0.9238 0.5010 0.6889
0.0433 2.16 10000 0.0471 0.8581 0.9103 0.9842 0.9951 0.9843 0.9617 0.9646 0.9416 0.9549 0.9718 0.6305 0.7879 0.9915 0.9644 0.9100 0.9155 0.8976 0.9145 0.9245 0.5127 0.6920
0.0412 2.26 10500 0.0468 0.8574 0.9042 0.9835 0.9956 0.9848 0.9628 0.9669 0.9023 0.9615 0.9677 0.6115 0.7847 0.9918 0.9601 0.9248 0.9286 0.8656 0.9177 0.9245 0.5073 0.6964
0.0489 2.37 11000 0.0496 0.8511 0.9029 0.9832 0.9956 0.9858 0.9905 0.9948 0.8694 0.9574 0.9654 0.5748 0.7926 0.9921 0.9604 0.9066 0.9086 0.8615 0.9167 0.9228 0.4913 0.7004
0.0388 2.48 11500 0.0450 0.8594 0.9036 0.9849 0.9957 0.9857 0.9621 0.9648 0.9620 0.9493 0.9604 0.5733 0.7793 0.9922 0.9649 0.9155 0.9205 0.9076 0.9138 0.9257 0.4941 0.7002
0.0409 2.59 12000 0.0493 0.8579 0.9124 0.9844 0.9955 0.9853 0.9928 0.9929 0.9083 0.9573 0.9671 0.6288 0.7832 0.9921 0.9651 0.9046 0.9086 0.8842 0.9196 0.9267 0.5175 0.7026
0.0477 2.7 12500 0.0436 0.8610 0.9051 0.9848 0.9957 0.9868 0.9639 0.9675 0.9478 0.9445 0.9590 0.5972 0.7831 0.9919 0.9654 0.9187 0.9251 0.9029 0.9126 0.9253 0.5035 0.7034
0.0488 2.8 13000 0.0450 0.8577 0.9076 0.9842 0.9963 0.9848 0.9712 0.9695 0.9132 0.9493 0.9621 0.6188 0.8026 0.9924 0.9635 0.9095 0.9124 0.8742 0.9172 0.9276 0.5157 0.7065
0.0879 2.91 13500 0.0516 0.8453 0.8949 0.9819 0.9960 0.9867 0.9631 0.9665 0.8325 0.9618 0.9678 0.6033 0.7763 0.9919 0.9574 0.8955 0.9007 0.8088 0.9206 0.9245 0.5069 0.7013
0.0525 3.02 14000 0.0474 0.8521 0.9053 0.9830 0.9959 0.9849 0.9850 0.9925 0.8703 0.9481 0.9597 0.6076 0.8038 0.9923 0.9600 0.9050 0.9099 0.8420 0.9143 0.9263 0.5148 0.7044
0.0455 3.13 14500 0.0435 0.8579 0.9111 0.9842 0.9953 0.9852 0.9646 0.9672 0.9255 0.9569 0.9654 0.6514 0.7888 0.9923 0.9642 0.8971 0.9055 0.8780 0.9182 0.9284 0.5327 0.7046
0.0454 3.24 15000 0.0451 0.8599 0.9161 0.9844 0.9953 0.9858 0.9895 0.9907 0.8944 0.9635 0.9692 0.6643 0.7925 0.9924 0.9645 0.9061 0.9107 0.8803 0.9202 0.9236 0.5356 0.7058
0.0687 3.34 15500 0.0496 0.8482 0.9017 0.9827 0.9959 0.9869 0.9715 0.9676 0.8483 0.9616 0.9672 0.6235 0.7932 0.9922 0.9614 0.8904 0.8909 0.8269 0.9187 0.9218 0.5249 0.7069
0.0555 3.45 16000 0.0445 0.8568 0.9081 0.9838 0.9964 0.9858 0.9649 0.9681 0.8880 0.9585 0.9610 0.6510 0.7995 0.9922 0.9635 0.8996 0.9073 0.8582 0.9230 0.9257 0.5328 0.7093
0.0528 3.56 16500 0.0477 0.8549 0.9053 0.9833 0.9958 0.9875 0.9668 0.9677 0.8740 0.9512 0.9631 0.6512 0.7902 0.9920 0.9618 0.9021 0.9036 0.8486 0.9185 0.9254 0.5348 0.7070
0.043 3.67 17000 0.0439 0.8633 0.9173 0.9849 0.9960 0.9851 0.9860 0.9893 0.9114 0.9555 0.9656 0.6623 0.8046 0.9921 0.9666 0.9083 0.9158 0.8910 0.9197 0.9262 0.5391 0.7111
0.0372 3.77 17500 0.0474 0.8555 0.9039 0.9836 0.9959 0.9876 0.9626 0.9647 0.8818 0.9556 0.9623 0.6393 0.7858 0.9921 0.9623 0.8999 0.9065 0.8526 0.9218 0.9264 0.5299 0.7082
0.0614 3.88 18000 0.0463 0.8564 0.9088 0.9839 0.9959 0.9853 0.9644 0.9662 0.9035 0.9569 0.9638 0.6413 0.8025 0.9921 0.9643 0.8967 0.9020 0.8607 0.9202 0.9276 0.5330 0.7111
0.0413 3.99 18500 0.0453 0.8579 0.9123 0.9841 0.9963 0.9848 0.9794 0.9828 0.8865 0.9613 0.9695 0.6526 0.7977 0.9922 0.9648 0.8991 0.9047 0.8629 0.9221 0.9274 0.5369 0.7112
0.0386 4.1 19000 0.0438 0.8578 0.9109 0.9842 0.9959 0.9844 0.9649 0.9667 0.9154 0.9580 0.9662 0.6408 0.8062 0.9924 0.9644 0.8973 0.9025 0.8683 0.9196 0.9279 0.5340 0.7134
0.0541 4.21 19500 0.0443 0.8577 0.9118 0.9840 0.9957 0.9847 0.9829 0.9872 0.8935 0.9594 0.9686 0.6265 0.8077 0.9921 0.9641 0.9017 0.9079 0.8621 0.9203 0.9277 0.5298 0.7133
0.0409 4.31 20000 0.0433 0.8560 0.9083 0.9840 0.9959 0.9860 0.9670 0.9687 0.9020 0.9578 0.9632 0.6421 0.7918 0.9922 0.9652 0.8921 0.8966 0.8633 0.9206 0.9278 0.5349 0.7117
0.0398 4.42 20500 0.0451 0.8581 0.9102 0.9840 0.9960 0.9859 0.9687 0.9685 0.8885 0.9597 0.9684 0.6554 0.8004 0.9922 0.9638 0.9000 0.9042 0.8595 0.9232 0.9266 0.5395 0.7144
0.038 4.53 21000 0.0464 0.8608 0.9123 0.9843 0.9959 0.9866 0.9885 0.9907 0.8739 0.9616 0.9678 0.6398 0.8056 0.9921 0.9639 0.9088 0.9160 0.8657 0.9238 0.9273 0.5347 0.7150
0.0295 4.64 21500 0.0433 0.8596 0.9094 0.9840 0.9960 0.9864 0.9641 0.9664 0.8985 0.9535 0.9582 0.6581 0.8033 0.9922 0.9633 0.9056 0.9102 0.8619 0.9195 0.9276 0.5408 0.7151
0.0318 4.75 22000 0.0439 0.8600 0.9127 0.9842 0.9964 0.9848 0.9665 0.9676 0.8929 0.9627 0.9689 0.6656 0.8089 0.9923 0.9643 0.9007 0.9080 0.8645 0.9223 0.9283 0.5444 0.7156
0.0377 4.85 22500 0.0429 0.8619 0.9125 0.9846 0.9963 0.9849 0.9633 0.9666 0.9115 0.9609 0.9689 0.6527 0.8069 0.9923 0.9654 0.9052 0.9104 0.8762 0.9217 0.9288 0.5407 0.7166
0.0419 4.96 23000 0.0433 0.8611 0.9107 0.9846 0.9964 0.9857 0.9654 0.9664 0.9065 0.9591 0.9662 0.6491 0.8015 0.9923 0.9655 0.9017 0.9085 0.8749 0.9223 0.9289 0.5394 0.7160

Framework versions

  • Transformers 4.35.2
  • Pytorch 2.1.1
  • Datasets 2.15.0
  • Tokenizers 0.15.0