đ segformer-b0-finetuned-morphpadver1-hgo-coord
This model is a fine - tuned version of [nvidia/mit - b0](https://huggingface.co/nvidia/mit - b0) on the NICOPOI - 9/morphpad_coord_hgo_512_4class dataset. It offers high - performance results in image segmentation tasks, achieving excellent scores on multiple evaluation metrics.
đ Quick Start
Evaluation Results
This model achieves the following results on the evaluation set:
- Loss: 0.0306
- Mean Iou: 0.9858
- Mean Accuracy: 0.9928
- Overall Accuracy: 0.9928
- Accuracy 0 - 0: 0.9933
- Accuracy 0 - 90: 0.9937
- Accuracy 90 - 0: 0.9943
- Accuracy 90 - 90: 0.9898
- Iou 0 - 0: 0.9885
- Iou 0 - 90: 0.9850
- Iou 90 - 0: 0.9826
- Iou 90 - 90: 0.9872
đ Documentation
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: 1
- eval_batch_size: 1
- seed: 42
- 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: linear
- num_epochs: 80
Training Results
Training Loss |
Epoch |
Step |
Validation Loss |
Mean Iou |
Mean Accuracy |
Overall Accuracy |
Accuracy 0 - 0 |
Accuracy 0 - 90 |
Accuracy 90 - 0 |
Accuracy 90 - 90 |
Iou 0 - 0 |
Iou 0 - 90 |
Iou 90 - 0 |
Iou 90 - 90 |
1.2185 |
2.5445 |
4000 |
1.2349 |
0.2290 |
0.3745 |
0.3762 |
0.2785 |
0.4062 |
0.4936 |
0.3198 |
0.2085 |
0.2334 |
0.2525 |
0.2216 |
1.0978 |
5.0891 |
8000 |
1.1020 |
0.2905 |
0.4487 |
0.4508 |
0.3780 |
0.5302 |
0.5341 |
0.3524 |
0.2937 |
0.2870 |
0.2991 |
0.2822 |
0.9886 |
7.6336 |
12000 |
1.0139 |
0.3231 |
0.4871 |
0.4896 |
0.4154 |
0.4500 |
0.7245 |
0.3585 |
0.3291 |
0.3272 |
0.3266 |
0.3096 |
0.9358 |
10.1781 |
16000 |
0.9575 |
0.3517 |
0.5195 |
0.5215 |
0.3765 |
0.6411 |
0.5865 |
0.4740 |
0.3438 |
0.3539 |
0.3617 |
0.3473 |
0.8735 |
12.7226 |
20000 |
0.8853 |
0.4007 |
0.5704 |
0.5726 |
0.4998 |
0.5637 |
0.7536 |
0.4647 |
0.4109 |
0.3953 |
0.4055 |
0.3913 |
0.7186 |
15.2672 |
24000 |
0.6833 |
0.5558 |
0.7151 |
0.7141 |
0.7389 |
0.6650 |
0.6919 |
0.7647 |
0.5919 |
0.5261 |
0.5453 |
0.5598 |
0.6514 |
17.8117 |
28000 |
0.4379 |
0.7017 |
0.8243 |
0.8243 |
0.8344 |
0.8161 |
0.8279 |
0.8187 |
0.7198 |
0.6807 |
0.6933 |
0.7130 |
0.603 |
20.3562 |
32000 |
0.2900 |
0.7980 |
0.8879 |
0.8874 |
0.9117 |
0.8490 |
0.8888 |
0.9020 |
0.8160 |
0.7726 |
0.7893 |
0.8142 |
0.2448 |
22.9008 |
36000 |
0.2154 |
0.8496 |
0.9184 |
0.9185 |
0.9330 |
0.9179 |
0.9170 |
0.9058 |
0.8683 |
0.8329 |
0.8445 |
0.8527 |
0.2766 |
25.4453 |
40000 |
0.2004 |
0.8612 |
0.9254 |
0.9254 |
0.9487 |
0.9059 |
0.9381 |
0.9088 |
0.8717 |
0.8469 |
0.8635 |
0.8628 |
0.6278 |
27.9898 |
44000 |
0.1410 |
0.8976 |
0.9459 |
0.9459 |
0.9426 |
0.9377 |
0.9559 |
0.9474 |
0.9075 |
0.8863 |
0.8932 |
0.9034 |
0.1684 |
30.5344 |
48000 |
0.1163 |
0.9137 |
0.9549 |
0.9548 |
0.9595 |
0.9417 |
0.9579 |
0.9605 |
0.9245 |
0.9046 |
0.9069 |
0.9187 |
0.0638 |
33.0789 |
52000 |
0.0927 |
0.9338 |
0.9657 |
0.9657 |
0.9697 |
0.9589 |
0.9715 |
0.9627 |
0.9406 |
0.9291 |
0.9291 |
0.9363 |
0.0749 |
35.6234 |
56000 |
0.0836 |
0.9382 |
0.9680 |
0.9680 |
0.9714 |
0.9663 |
0.9680 |
0.9664 |
0.9449 |
0.9325 |
0.9339 |
0.9414 |
0.045 |
38.1679 |
60000 |
0.0624 |
0.9545 |
0.9767 |
0.9767 |
0.9787 |
0.9751 |
0.9763 |
0.9766 |
0.9587 |
0.9521 |
0.9499 |
0.9573 |
0.1278 |
40.7125 |
64000 |
0.0635 |
0.9546 |
0.9767 |
0.9767 |
0.9773 |
0.9743 |
0.9813 |
0.9737 |
0.9598 |
0.9521 |
0.9492 |
0.9572 |
0.0443 |
43.2570 |
68000 |
0.0598 |
0.9584 |
0.9787 |
0.9787 |
0.9815 |
0.9723 |
0.9858 |
0.9752 |
0.9624 |
0.9548 |
0.9548 |
0.9617 |
0.0337 |
45.8015 |
72000 |
0.0549 |
0.9622 |
0.9807 |
0.9807 |
0.9877 |
0.9804 |
0.9820 |
0.9726 |
0.9648 |
0.9587 |
0.9622 |
0.9632 |
0.0434 |
48.3461 |
76000 |
0.0539 |
0.9643 |
0.9816 |
0.9817 |
0.9793 |
0.9779 |
0.9913 |
0.9781 |
0.9691 |
0.9611 |
0.9565 |
0.9703 |
0.1576 |
50.8906 |
80000 |
0.0577 |
0.9656 |
0.9825 |
0.9825 |
0.9799 |
0.9822 |
0.9825 |
0.9856 |
0.9694 |
0.9634 |
0.9653 |
0.9645 |
0.025 |
53.4351 |
84000 |
0.0453 |
0.9724 |
0.9860 |
0.9860 |
0.9856 |
0.9884 |
0.9840 |
0.9858 |
0.9762 |
0.9698 |
0.9697 |
0.9739 |
0.0318 |
55.9796 |
88000 |
0.0401 |
0.9733 |
0.9865 |
0.9865 |
0.9884 |
0.9845 |
0.9865 |
0.9865 |
0.9766 |
0.9700 |
0.9714 |
0.9753 |
0.1355 |
58.5242 |
92000 |
0.0453 |
0.9764 |
0.9880 |
0.9880 |
0.9896 |
0.9874 |
0.9889 |
0.9861 |
0.9796 |
0.9742 |
0.9731 |
0.9786 |
0.0256 |
61.0687 |
96000 |
0.0359 |
0.9817 |
0.9907 |
0.9908 |
0.9902 |
0.9925 |
0.9902 |
0.9901 |
0.9846 |
0.9808 |
0.9783 |
0.9833 |
0.019 |
63.6132 |
100000 |
0.0320 |
0.9819 |
0.9908 |
0.9909 |
0.9914 |
0.9908 |
0.9936 |
0.9875 |
0.9838 |
0.9812 |
0.9787 |
0.9841 |
0.0713 |
66.1578 |
104000 |
0.0319 |
0.9827 |
0.9912 |
0.9912 |
0.9940 |
0.9922 |
0.9937 |
0.9847 |
0.9859 |
0.9812 |
0.9807 |
0.9828 |
0.1036 |
68.7023 |
108000 |
0.0369 |
0.9807 |
0.9902 |
0.9903 |
0.9932 |
0.9916 |
0.9946 |
0.9813 |
0.9844 |
0.9807 |
0.9790 |
0.9788 |
0.0575 |
71.2468 |
112000 |
0.0338 |
0.9843 |
0.9921 |
0.9921 |
0.9939 |
0.9913 |
0.9929 |
0.9901 |
0.9870 |
0.9822 |
0.9814 |
0.9867 |
0.0136 |
73.7913 |
116000 |
0.0259 |
0.9870 |
0.9934 |
0.9934 |
0.9926 |
0.9936 |
0.9946 |
0.9930 |
0.9889 |
0.9852 |
0.9850 |
0.9891 |
0.045 |
76.3359 |
120000 |
0.0310 |
0.9844 |
0.9921 |
0.9921 |
0.9913 |
0.9926 |
0.9941 |
0.9902 |
0.9866 |
0.9834 |
0.9805 |
0.9871 |
0.6665 |
78.8804 |
124000 |
0.0306 |
0.9858 |
0.9928 |
0.9928 |
0.9933 |
0.9937 |
0.9943 |
0.9898 |
0.9885 |
0.9850 |
0.9826 |
0.9872 |
Framework Versions
- Transformers 4.48.3
- Pytorch 2.1.0
- Datasets 3.2.0
- Tokenizers 0.21.0
đ License
The license for this model is 'other'.