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TommyClas
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phaseseg_models

Image Segmentation
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phaseseg_models

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

  • Loss: 0.0372
  • Mean Iou: 0.9744
  • Mean Accuracy: 0.9872
  • Overall Accuracy: 0.9869
  • Accuracy ่ƒŒๆ™ฏ: nan
  • Accuracy ๆœชๆฐดๅŒ–ๆฐดๆณฅ้ข—็ฒ’: 0.9806
  • Accuracy ๅญ”้š™: 0.9893
  • Accuracy ๆฐขๆฐงๅŒ–้’™: 0.9901
  • Accuracy ๅ…ถไป–ๆฐดๅŒ–็‰ฉ: 0.9887
  • Iou ่ƒŒๆ™ฏ: nan
  • Iou ๆœชๆฐดๅŒ–ๆฐดๆณฅ้ข—็ฒ’: 0.9730
  • Iou ๅญ”้š™: 0.9695
  • Iou ๆฐขๆฐงๅŒ–้’™: 0.9767
  • Iou ๅ…ถไป–ๆฐดๅŒ–็‰ฉ: 0.9782

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: 8
  • eval_batch_size: 8
  • seed: 1337
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: polynomial
  • training_steps: 10000

Training results

Training Loss Epoch Step Validation Loss Mean Iou Mean Accuracy Overall Accuracy Accuracy ่ƒŒๆ™ฏ Accuracy ๆœชๆฐดๅŒ–ๆฐดๆณฅ้ข—็ฒ’ Accuracy ๅญ”้š™ Accuracy ๆฐขๆฐงๅŒ–้’™ Accuracy ๅ…ถไป–ๆฐดๅŒ–็‰ฉ Iou ่ƒŒๆ™ฏ Iou ๆœชๆฐดๅŒ–ๆฐดๆณฅ้ข—็ฒ’ Iou ๅญ”้š™ Iou ๆฐขๆฐงๅŒ–้’™ Iou ๅ…ถไป–ๆฐดๅŒ–็‰ฉ
No log 1.0 50 0.2994 0.7375 0.9586 0.9580 nan 0.9557 0.9290 0.9696 0.9802 0.0 0.9224 0.9066 0.9195 0.9392
0.4501 2.0 100 0.1558 0.7645 0.9767 0.9766 nan 0.9802 0.9580 0.9758 0.9929 0.0 0.9585 0.9504 0.9524 0.9609
0.4501 3.0 150 0.1193 0.7715 0.9814 0.9812 nan 0.9797 0.9718 0.9829 0.9912 0.0 0.9661 0.9628 0.9607 0.9680
0.0949 4.0 200 0.0898 0.7745 0.9835 0.9834 nan 0.9844 0.9751 0.9842 0.9902 0.0 0.9702 0.9667 0.9655 0.9699
0.0949 5.0 250 0.0766 0.7762 0.9848 0.9848 nan 0.9848 0.9799 0.9842 0.9905 0.0 0.9729 0.9696 0.9674 0.9713
0.0584 6.0 300 0.0624 0.7771 0.9856 0.9855 nan 0.9865 0.9802 0.9852 0.9905 0.0 0.9747 0.9704 0.9684 0.9723
0.0584 7.0 350 0.0628 0.7777 0.9859 0.9858 nan 0.9845 0.9817 0.9865 0.9907 0.0 0.9743 0.9717 0.9695 0.9731
0.0441 8.0 400 0.0575 0.7784 0.9863 0.9863 nan 0.9852 0.9841 0.9846 0.9914 0.0 0.9750 0.9732 0.9709 0.9732
0.0441 9.0 450 0.0500 0.7788 0.9867 0.9866 nan 0.9855 0.9847 0.9839 0.9925 0.0 0.9762 0.9738 0.9706 0.9734
0.0363 10.0 500 0.0496 0.7795 0.9870 0.9869 nan 0.9841 0.9859 0.9875 0.9905 0.0 0.9753 0.9745 0.9726 0.9751
0.0363 11.0 550 0.0458 0.7798 0.9873 0.9872 nan 0.9844 0.9863 0.9875 0.9910 0.0 0.9758 0.9749 0.9731 0.9755
0.0315 12.0 600 0.0423 0.7802 0.9875 0.9875 nan 0.9872 0.9845 0.9895 0.9891 0.0 0.9771 0.9750 0.9731 0.9757
0.0315 13.0 650 0.0437 0.7800 0.9874 0.9873 nan 0.9851 0.9848 0.9891 0.9908 0.0 0.9762 0.9749 0.9731 0.9760
0.0278 14.0 700 0.0390 0.7805 0.9878 0.9877 nan 0.9862 0.9859 0.9874 0.9916 0.0 0.9772 0.9753 0.9738 0.9762
0.0278 15.0 750 0.0404 0.7799 0.9874 0.9873 nan 0.9834 0.9872 0.9896 0.9896 0.0 0.9753 0.9738 0.9740 0.9764
0.0255 16.0 800 0.0422 0.7789 0.9868 0.9866 nan 0.9809 0.9872 0.9878 0.9911 0.0 0.9725 0.9709 0.9745 0.9765
0.0255 17.0 850 0.0387 0.7794 0.9871 0.9869 nan 0.9831 0.9858 0.9900 0.9895 0.0 0.9742 0.9720 0.9739 0.9767
0.0235 18.0 900 0.0395 0.7791 0.9869 0.9867 nan 0.9810 0.9882 0.9881 0.9903 0.0 0.9725 0.9706 0.9751 0.9770
0.0235 19.0 950 0.0364 0.7790 0.9868 0.9866 nan 0.9809 0.9886 0.9867 0.9911 0.0 0.9723 0.9706 0.9752 0.9769
0.0221 20.0 1000 0.0394 0.7785 0.9865 0.9863 nan 0.9801 0.9870 0.9887 0.9904 0.0 0.9713 0.9691 0.9751 0.9769
0.0221 21.0 1050 0.0374 0.7787 0.9866 0.9864 nan 0.9812 0.9873 0.9871 0.9910 0.0 0.9720 0.9697 0.9750 0.9768
0.021 22.0 1100 0.0364 0.7787 0.9867 0.9865 nan 0.9804 0.9874 0.9884 0.9906 0.0 0.9718 0.9695 0.9753 0.9771
0.021 23.0 1150 0.0375 0.7784 0.9865 0.9863 nan 0.9792 0.9883 0.9888 0.9897 0.0 0.9708 0.9687 0.9754 0.9774
0.0199 24.0 1200 0.0371 0.7782 0.9864 0.9861 nan 0.9792 0.9871 0.9878 0.9913 0.0 0.9709 0.9684 0.9749 0.9768
0.0199 25.0 1250 0.0393 0.7784 0.9865 0.9862 nan 0.9788 0.9885 0.9890 0.9897 0.0 0.9707 0.9683 0.9754 0.9776
0.0191 26.0 1300 0.0387 0.7783 0.9865 0.9862 nan 0.9791 0.9878 0.9904 0.9887 0.0 0.9709 0.9683 0.9750 0.9775
0.0191 27.0 1350 0.0384 0.7785 0.9865 0.9863 nan 0.9794 0.9880 0.9897 0.9890 0.0 0.9711 0.9685 0.9754 0.9775
0.0188 28.0 1400 0.0383 0.7783 0.9865 0.9862 nan 0.9779 0.9893 0.9884 0.9903 0.0 0.9705 0.9682 0.9754 0.9776
0.0188 29.0 1450 0.0377 0.7784 0.9864 0.9862 nan 0.9785 0.9902 0.9890 0.9880 0.0 0.9703 0.9680 0.9759 0.9775
0.018 30.0 1500 0.0378 0.9732 0.9866 0.9863 nan 0.9794 0.9885 0.9888 0.9895 nan 0.9710 0.9683 0.9757 0.9777
0.018 31.0 1550 0.0379 0.9730 0.9865 0.9862 nan 0.9794 0.9875 0.9901 0.9890 nan 0.9710 0.9681 0.9753 0.9776
0.0175 32.0 1600 0.0381 0.9730 0.9865 0.9862 nan 0.9792 0.9884 0.9894 0.9889 nan 0.9708 0.9682 0.9755 0.9775
0.0175 33.0 1650 0.0394 0.7784 0.9865 0.9862 nan 0.9783 0.9896 0.9894 0.9886 0.0 0.9705 0.9679 0.9758 0.9777
0.0171 34.0 1700 0.0390 0.7784 0.9865 0.9863 nan 0.9800 0.9871 0.9902 0.9887 0.0 0.9712 0.9682 0.9753 0.9775
0.0171 35.0 1750 0.0385 0.9729 0.9865 0.9862 nan 0.9790 0.9878 0.9892 0.9899 nan 0.9710 0.9680 0.9754 0.9774
0.0166 36.0 1800 0.0384 0.9731 0.9865 0.9863 nan 0.9791 0.9884 0.9889 0.9897 nan 0.9711 0.9682 0.9756 0.9775
0.0166 37.0 1850 0.0389 0.9730 0.9865 0.9862 nan 0.9794 0.9875 0.9891 0.9898 nan 0.9711 0.9680 0.9754 0.9775
0.0162 38.0 1900 0.0375 0.9731 0.9865 0.9863 nan 0.9797 0.9879 0.9901 0.9884 nan 0.9711 0.9681 0.9755 0.9777
0.0162 39.0 1950 0.0389 0.9731 0.9866 0.9863 nan 0.9786 0.9891 0.9891 0.9894 nan 0.9709 0.9681 0.9759 0.9776
0.0158 40.0 2000 0.0396 0.9730 0.9865 0.9862 nan 0.9783 0.9897 0.9894 0.9886 nan 0.9705 0.9678 0.9761 0.9777
0.0158 41.0 2050 0.0397 0.7784 0.9865 0.9862 nan 0.9788 0.9889 0.9887 0.9895 0.0 0.9708 0.9679 0.9759 0.9773
0.0156 42.0 2100 0.0401 0.9730 0.9865 0.9862 nan 0.9782 0.9889 0.9890 0.9898 nan 0.9707 0.9678 0.9758 0.9775
0.0156 43.0 2150 0.0399 0.9730 0.9865 0.9862 nan 0.9789 0.9886 0.9896 0.9889 nan 0.9708 0.9678 0.9757 0.9777
0.0154 44.0 2200 0.0407 0.9728 0.9864 0.9861 nan 0.9781 0.9900 0.9884 0.9891 nan 0.9702 0.9673 0.9762 0.9776
0.0154 45.0 2250 0.0405 0.7784 0.9865 0.9862 nan 0.9785 0.9901 0.9896 0.9877 0.0 0.9706 0.9675 0.9761 0.9776
0.0151 46.0 2300 0.0411 0.7782 0.9864 0.9861 nan 0.9784 0.9903 0.9901 0.9866 0.0 0.9704 0.9673 0.9758 0.9775
0.0151 47.0 2350 0.0394 0.9732 0.9866 0.9863 nan 0.9790 0.9896 0.9890 0.9886 nan 0.9709 0.9681 0.9759 0.9777
0.015 48.0 2400 0.0405 0.7784 0.9865 0.9863 nan 0.9787 0.9885 0.9892 0.9898 0.0 0.9708 0.9677 0.9757 0.9780
0.015 49.0 2450 0.0399 0.7786 0.9866 0.9863 nan 0.9787 0.9905 0.9882 0.9888 0.0 0.9707 0.9678 0.9764 0.9779
0.0149 50.0 2500 0.0410 0.7783 0.9864 0.9861 nan 0.9781 0.9895 0.9889 0.9891 0.0 0.9705 0.9673 0.9761 0.9776
0.0149 51.0 2550 0.0405 0.7784 0.9865 0.9862 nan 0.9793 0.9898 0.9895 0.9872 0.0 0.9707 0.9676 0.9763 0.9776
0.0145 52.0 2600 0.0402 0.7785 0.9866 0.9863 nan 0.9788 0.9895 0.9893 0.9887 0.0 0.9710 0.9678 0.9760 0.9778
0.0145 53.0 2650 0.0401 0.7786 0.9866 0.9864 nan 0.9791 0.9889 0.9898 0.9887 0.0 0.9710 0.9680 0.9761 0.9780
0.0144 54.0 2700 0.0392 0.7787 0.9867 0.9864 nan 0.9795 0.9888 0.9887 0.9896 0.0 0.9714 0.9682 0.9761 0.9777
0.0144 55.0 2750 0.0409 0.7784 0.9865 0.9862 nan 0.9787 0.9886 0.9895 0.9891 0.0 0.9706 0.9675 0.9760 0.9777
0.0141 56.0 2800 0.0410 0.7784 0.9865 0.9862 nan 0.9779 0.9897 0.9897 0.9887 0.0 0.9707 0.9675 0.9759 0.9778
0.0141 57.0 2850 0.0412 0.7784 0.9865 0.9862 nan 0.9780 0.9898 0.9891 0.9891 0.0 0.9707 0.9676 0.9761 0.9776
0.014 58.0 2900 0.0403 0.9732 0.9866 0.9863 nan 0.9794 0.9889 0.9889 0.9891 nan 0.9713 0.9680 0.9761 0.9775
0.014 59.0 2950 0.0404 0.7787 0.9867 0.9864 nan 0.9787 0.9899 0.9889 0.9892 0.0 0.9711 0.9680 0.9763 0.9779
0.0139 60.0 3000 0.0412 0.7783 0.9865 0.9862 nan 0.9786 0.9893 0.9900 0.9879 0.0 0.9708 0.9675 0.9758 0.9775
0.0139 61.0 3050 0.0410 0.7785 0.9866 0.9863 nan 0.9789 0.9893 0.9901 0.9879 0.0 0.9708 0.9676 0.9762 0.9780
0.0138 62.0 3100 0.0413 0.7784 0.9865 0.9862 nan 0.9778 0.9896 0.9893 0.9894 0.0 0.9705 0.9675 0.9763 0.9779
0.0138 63.0 3150 0.0400 0.7786 0.9866 0.9863 nan 0.9794 0.9887 0.9908 0.9874 0.0 0.9715 0.9681 0.9757 0.9776
0.0138 64.0 3200 0.0401 0.7786 0.9866 0.9864 nan 0.9800 0.9888 0.9904 0.9873 0.0 0.9715 0.9682 0.9758 0.9776
0.0138 65.0 3250 0.0414 0.7784 0.9865 0.9862 nan 0.9788 0.9888 0.9905 0.9879 0.0 0.9708 0.9675 0.9759 0.9776
0.0136 66.0 3300 0.0397 0.7787 0.9867 0.9864 nan 0.9796 0.9895 0.9897 0.9880 0.0 0.9714 0.9683 0.9763 0.9776
0.0136 67.0 3350 0.0417 0.7783 0.9864 0.9861 nan 0.9777 0.9894 0.9903 0.9884 0.0 0.9702 0.9671 0.9761 0.9779
0.0135 68.0 3400 0.0409 0.7784 0.9865 0.9862 nan 0.9790 0.9898 0.9908 0.9862 0.0 0.9711 0.9678 0.9758 0.9773
0.0135 69.0 3450 0.0399 0.7787 0.9867 0.9864 nan 0.9796 0.9896 0.9887 0.9888 0.0 0.9714 0.9681 0.9764 0.9778
0.0133 70.0 3500 0.0407 0.7785 0.9865 0.9863 nan 0.9792 0.9903 0.9901 0.9866 0.0 0.9713 0.9676 0.9761 0.9775
0.0133 71.0 3550 0.0407 0.7786 0.9866 0.9864 nan 0.9787 0.9896 0.9892 0.9890 0.0 0.9712 0.9679 0.9761 0.9778
0.0131 72.0 3600 0.0394 0.7789 0.9868 0.9865 nan 0.9790 0.9899 0.9895 0.9887 0.0 0.9714 0.9681 0.9766 0.9781
0.0131 73.0 3650 0.0410 0.7785 0.9865 0.9863 nan 0.9796 0.9897 0.9903 0.9865 0.0 0.9713 0.9678 0.9759 0.9774
0.0132 74.0 3700 0.0412 0.7785 0.9866 0.9863 nan 0.9791 0.9900 0.9901 0.9871 0.0 0.9713 0.9678 0.9761 0.9774
0.0132 75.0 3750 0.0412 0.7786 0.9866 0.9863 nan 0.9785 0.9902 0.9898 0.9879 0.0 0.9711 0.9676 0.9763 0.9779
0.0131 76.0 3800 0.0396 0.7786 0.9866 0.9864 nan 0.9798 0.9893 0.9904 0.9870 0.0 0.9716 0.9682 0.9760 0.9775
0.0131 77.0 3850 0.0418 0.7784 0.9865 0.9862 nan 0.9789 0.9896 0.9905 0.9871 0.0 0.9711 0.9676 0.9760 0.9775
0.013 78.0 3900 0.0396 0.7786 0.9866 0.9864 nan 0.9787 0.9899 0.9906 0.9872 0.0 0.9713 0.9678 0.9760 0.9779
0.013 79.0 3950 0.0398 0.7787 0.9867 0.9864 nan 0.9794 0.9898 0.9905 0.9869 0.0 0.9715 0.9680 0.9762 0.9777
0.0128 80.0 4000 0.0402 0.7788 0.9867 0.9865 nan 0.9789 0.9898 0.9896 0.9885 0.0 0.9714 0.9680 0.9765 0.9779
0.0128 81.0 4050 0.0404 0.7787 0.9867 0.9864 nan 0.9787 0.9903 0.9902 0.9874 0.0 0.9713 0.9677 0.9763 0.9779
0.0127 82.0 4100 0.0397 0.7787 0.9867 0.9864 nan 0.9794 0.9896 0.9901 0.9877 0.0 0.9716 0.9681 0.9762 0.9778
0.0127 83.0 4150 0.0411 0.7786 0.9866 0.9863 nan 0.9786 0.9898 0.9899 0.9881 0.0 0.9712 0.9677 0.9763 0.9778
0.0127 84.0 4200 0.0406 0.7788 0.9867 0.9865 nan 0.9787 0.9903 0.9890 0.9889 0.0 0.9713 0.9680 0.9766 0.9781
0.0127 85.0 4250 0.0413 0.7786 0.9866 0.9864 nan 0.9787 0.9900 0.9888 0.9891 0.0 0.9711 0.9677 0.9764 0.9779
0.0126 86.0 4300 0.0400 0.7788 0.9867 0.9865 nan 0.9792 0.9904 0.9895 0.9878 0.0 0.9715 0.9681 0.9765 0.9778
0.0126 87.0 4350 0.0397 0.7788 0.9868 0.9865 nan 0.9789 0.9898 0.9898 0.9885 0.0 0.9715 0.9682 0.9765 0.9780
0.0125 88.0 4400 0.0398 0.7788 0.9868 0.9865 nan 0.9791 0.9903 0.9894 0.9883 0.0 0.9716 0.9681 0.9767 0.9779
0.0125 89.0 4450 0.0400 0.7787 0.9867 0.9865 nan 0.9795 0.9898 0.9902 0.9872 0.0 0.9716 0.9682 0.9763 0.9776
0.0125 90.0 4500 0.0397 0.7788 0.9867 0.9865 nan 0.9788 0.9902 0.9893 0.9887 0.0 0.9716 0.9680 0.9765 0.9779
0.0125 91.0 4550 0.0400 0.7787 0.9867 0.9864 nan 0.9790 0.9901 0.9903 0.9875 0.0 0.9715 0.9680 0.9762 0.9779
0.0125 92.0 4600 0.0392 0.7787 0.9867 0.9864 nan 0.9790 0.9903 0.9898 0.9878 0.0 0.9716 0.9680 0.9765 0.9777
0.0125 93.0 4650 0.0403 0.7787 0.9867 0.9864 nan 0.9791 0.9900 0.9905 0.9873 0.0 0.9716 0.9681 0.9763 0.9777
0.0123 94.0 4700 0.0396 0.7789 0.9868 0.9865 nan 0.9797 0.9898 0.9903 0.9874 0.0 0.9718 0.9684 0.9764 0.9778
0.0123 95.0 4750 0.0405 0.7787 0.9867 0.9864 nan 0.9790 0.9901 0.9903 0.9874 0.0 0.9715 0.9679 0.9764 0.9778
0.0122 96.0 4800 0.0394 0.7789 0.9868 0.9865 nan 0.9793 0.9896 0.9898 0.9884 0.0 0.9717 0.9682 0.9764 0.9780
0.0122 97.0 4850 0.0396 0.7789 0.9868 0.9865 nan 0.9790 0.9900 0.9900 0.9882 0.0 0.9716 0.9681 0.9766 0.9780
0.0122 98.0 4900 0.0399 0.7788 0.9867 0.9865 nan 0.9797 0.9900 0.9904 0.9870 0.0 0.9718 0.9682 0.9764 0.9776
0.0122 99.0 4950 0.0394 0.7789 0.9868 0.9865 nan 0.9793 0.9896 0.9897 0.9885 0.0 0.9717 0.9682 0.9766 0.9780
0.0122 100.0 5000 0.0383 0.7790 0.9868 0.9866 nan 0.9804 0.9899 0.9895 0.9876 0.0 0.9720 0.9686 0.9767 0.9777
0.0122 101.0 5050 0.0399 0.7788 0.9867 0.9865 nan 0.9794 0.9904 0.9895 0.9877 0.0 0.9716 0.9680 0.9766 0.9779
0.0121 102.0 5100 0.0392 0.7790 0.9868 0.9866 nan 0.9796 0.9898 0.9889 0.9890 0.0 0.9718 0.9685 0.9767 0.9779
0.0121 103.0 5150 0.0393 0.7788 0.9867 0.9865 nan 0.9788 0.9901 0.9900 0.9881 0.0 0.9715 0.9679 0.9765 0.9781
0.012 104.0 5200 0.0400 0.7788 0.9867 0.9865 nan 0.9790 0.9894 0.9904 0.9881 0.0 0.9716 0.9682 0.9763 0.9779
0.012 105.0 5250 0.0393 0.7789 0.9868 0.9865 nan 0.9796 0.9894 0.9904 0.9878 0.0 0.9718 0.9683 0.9764 0.9780
0.012 106.0 5300 0.0390 0.7789 0.9868 0.9866 nan 0.9794 0.9900 0.9890 0.9888 0.0 0.9719 0.9683 0.9766 0.9780
0.012 107.0 5350 0.0383 0.7790 0.9868 0.9866 nan 0.9801 0.9899 0.9903 0.9870 0.0 0.9721 0.9684 0.9765 0.9779
0.0119 108.0 5400 0.0380 0.7792 0.9870 0.9868 nan 0.9807 0.9892 0.9897 0.9883 0.0 0.9724 0.9690 0.9768 0.9780
0.0119 109.0 5450 0.0400 0.7787 0.9867 0.9864 nan 0.9786 0.9902 0.9902 0.9876 0.0 0.9714 0.9677 0.9764 0.9778
0.0119 110.0 5500 0.0385 0.7791 0.9869 0.9867 nan 0.9801 0.9894 0.9891 0.9889 0.0 0.9721 0.9686 0.9768 0.9780
0.0119 111.0 5550 0.0385 0.7790 0.9869 0.9866 nan 0.9798 0.9896 0.9902 0.9879 0.0 0.9719 0.9685 0.9767 0.9781
0.0118 112.0 5600 0.0377 0.7791 0.9869 0.9867 nan 0.9798 0.9891 0.9897 0.9891 0.0 0.9722 0.9687 0.9766 0.9782
0.0118 113.0 5650 0.0388 0.7790 0.9869 0.9866 nan 0.9794 0.9899 0.9904 0.9878 0.0 0.9719 0.9683 0.9767 0.9781
0.0118 114.0 5700 0.0391 0.7789 0.9868 0.9866 nan 0.9797 0.9891 0.9906 0.9880 0.0 0.9719 0.9683 0.9763 0.9781
0.0118 115.0 5750 0.0390 0.7789 0.9868 0.9866 nan 0.9796 0.9902 0.9899 0.9876 0.0 0.9719 0.9683 0.9766 0.9779
0.0118 116.0 5800 0.0390 0.7789 0.9868 0.9866 nan 0.9795 0.9899 0.9896 0.9882 0.0 0.9718 0.9682 0.9767 0.9779
0.0118 117.0 5850 0.0394 0.7788 0.9867 0.9865 nan 0.9791 0.9899 0.9896 0.9883 0.0 0.9717 0.9679 0.9765 0.9778
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Framework versions

  • Transformers 4.52.0.dev0
  • Pytorch 2.6.0+cu124
  • Datasets 3.5.0
  • Tokenizers 0.21.1
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