đ segformer-b2-cloth-parse-9
This model is a fine - tuned version of mattmdjaga/segformer_b2_clothes on the cloth_parsing_mix dataset. It offers high - performance image segmentation capabilities for clothing, achieving excellent results on the evaluation set.
đ Quick Start
This model is ready for use in image segmentation tasks. You can load it using relevant libraries and start making predictions.
đ Documentation
Model Information
Property |
Details |
Model Type |
Fine - tuned version of mattmdjaga/segformer_b2_clothes |
Training Data |
cloth_parsing_mix |
Pipeline Tag |
image - segmentation |
Evaluation Results
This model 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
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 |
0.57 |
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đ License
This model is released under the MIT license.