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---
library_name: transformers
license: other
base_model: nvidia/mit-b3
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b0-finetuned-morphpadver1-hgo-coord-v3_1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b0-finetuned-morphpadver1-hgo-coord-v3_1
This model is a fine-tuned version of [nvidia/mit-b3](https://huggingface.co/nvidia/mit-b3) on the NICOPOI-9/morphpad_coord_hgo_512_4class_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0117
- Mean Iou: 0.9981
- Mean Accuracy: 0.9990
- Overall Accuracy: 0.9990
- Accuracy 0-0: 0.9995
- Accuracy 0-90: 0.9985
- Accuracy 90-0: 0.9988
- Accuracy 90-90: 0.9993
- Iou 0-0: 0.9991
- Iou 0-90: 0.9979
- Iou 90-0: 0.9976
- Iou 90-90: 0.9978
## 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: 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: 60
### 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 |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:------------:|:-------------:|:-------------:|:--------------:|:-------:|:--------:|:--------:|:---------:|
| 0.903 | 2.6525 | 4000 | 0.8952 | 0.3916 | 0.5570 | 0.5567 | 0.5335 | 0.6125 | 0.4890 | 0.5929 | 0.4534 | 0.3418 | 0.3411 | 0.4299 |
| 0.6373 | 5.3050 | 8000 | 0.5078 | 0.6237 | 0.7643 | 0.7643 | 0.7676 | 0.8339 | 0.6741 | 0.7817 | 0.6758 | 0.5472 | 0.6022 | 0.6698 |
| 0.2851 | 7.9576 | 12000 | 0.2955 | 0.7612 | 0.8642 | 0.8642 | 0.8669 | 0.8687 | 0.8339 | 0.8874 | 0.7959 | 0.7358 | 0.7500 | 0.7631 |
| 0.2309 | 10.6101 | 16000 | 0.1305 | 0.9184 | 0.9574 | 0.9574 | 0.9575 | 0.9381 | 0.9648 | 0.9692 | 0.9333 | 0.9074 | 0.8991 | 0.9337 |
| 0.0907 | 13.2626 | 20000 | 0.1249 | 0.9267 | 0.9620 | 0.9620 | 0.9636 | 0.9541 | 0.9594 | 0.9708 | 0.9379 | 0.9205 | 0.9169 | 0.9316 |
| 0.3051 | 15.9151 | 24000 | 0.0529 | 0.9675 | 0.9835 | 0.9835 | 0.9842 | 0.9805 | 0.9839 | 0.9854 | 0.9712 | 0.9636 | 0.9626 | 0.9728 |
| 0.0659 | 18.5676 | 28000 | 0.0630 | 0.9670 | 0.9832 | 0.9833 | 0.9852 | 0.9747 | 0.9885 | 0.9846 | 0.9719 | 0.9642 | 0.9633 | 0.9687 |
| 0.0474 | 21.2202 | 32000 | 0.0454 | 0.9768 | 0.9882 | 0.9883 | 0.9910 | 0.9856 | 0.9865 | 0.9899 | 0.9783 | 0.9737 | 0.9747 | 0.9805 |
| 0.0449 | 23.8727 | 36000 | 0.0468 | 0.9795 | 0.9896 | 0.9896 | 0.9900 | 0.9812 | 0.9900 | 0.9973 | 0.9828 | 0.9743 | 0.9783 | 0.9824 |
| 0.0552 | 26.5252 | 40000 | 0.0266 | 0.9884 | 0.9942 | 0.9942 | 0.9949 | 0.9917 | 0.9947 | 0.9953 | 0.9888 | 0.9865 | 0.9866 | 0.9916 |
| 0.0541 | 29.1777 | 44000 | 0.0290 | 0.9908 | 0.9954 | 0.9954 | 0.9951 | 0.9951 | 0.9967 | 0.9946 | 0.9921 | 0.9897 | 0.9905 | 0.9909 |
| 0.0082 | 31.8302 | 48000 | 0.0421 | 0.9891 | 0.9945 | 0.9945 | 0.9940 | 0.9924 | 0.9951 | 0.9966 | 0.9908 | 0.9869 | 0.9884 | 0.9904 |
| 0.0061 | 34.4828 | 52000 | 0.0345 | 0.9923 | 0.9961 | 0.9961 | 0.9971 | 0.9941 | 0.9966 | 0.9966 | 0.9939 | 0.9912 | 0.9916 | 0.9922 |
| 0.0053 | 37.1353 | 56000 | 0.0256 | 0.9941 | 0.9970 | 0.9970 | 0.9976 | 0.9972 | 0.9966 | 0.9968 | 0.9957 | 0.9928 | 0.9929 | 0.9949 |
| 0.0045 | 39.7878 | 60000 | 0.0256 | 0.9937 | 0.9968 | 0.9968 | 0.9978 | 0.9959 | 0.9959 | 0.9978 | 0.9937 | 0.9927 | 0.9926 | 0.9957 |
| 0.0046 | 42.4403 | 64000 | 0.0171 | 0.9964 | 0.9982 | 0.9982 | 0.9983 | 0.9976 | 0.9987 | 0.9981 | 0.9972 | 0.9958 | 0.9955 | 0.9969 |
| 0.0032 | 45.0928 | 68000 | 0.0293 | 0.9957 | 0.9979 | 0.9979 | 0.9983 | 0.9969 | 0.9975 | 0.9988 | 0.9966 | 0.9950 | 0.9950 | 0.9964 |
| 0.003 | 47.7454 | 72000 | 0.0251 | 0.9964 | 0.9982 | 0.9982 | 0.9984 | 0.9973 | 0.9984 | 0.9987 | 0.9973 | 0.9952 | 0.9965 | 0.9966 |
| 0.0035 | 50.3979 | 76000 | 0.0245 | 0.9973 | 0.9986 | 0.9986 | 0.9993 | 0.9982 | 0.9983 | 0.9987 | 0.9982 | 0.9969 | 0.9963 | 0.9977 |
| 0.0025 | 53.0504 | 80000 | 0.0222 | 0.9972 | 0.9986 | 0.9986 | 0.9990 | 0.9980 | 0.9987 | 0.9986 | 0.9985 | 0.9965 | 0.9970 | 0.9968 |
| 0.0023 | 55.7029 | 84000 | 0.0104 | 0.9982 | 0.9991 | 0.9991 | 0.9994 | 0.9989 | 0.9987 | 0.9993 | 0.9988 | 0.9980 | 0.9975 | 0.9983 |
| 0.0022 | 58.3554 | 88000 | 0.0117 | 0.9981 | 0.9990 | 0.9990 | 0.9995 | 0.9985 | 0.9988 | 0.9993 | 0.9991 | 0.9979 | 0.9976 | 0.9978 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.1.0
- Datasets 3.2.0
- Tokenizers 0.21.0
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