File size: 5,245 Bytes
c12c25c 01f520b c12c25c 01f520b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 |
---
license: other
base_model: nvidia/mit-b4
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer_Clean_Set1_95images
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_Clean_Set1_95images
This model is a fine-tuned version of [nvidia/mit-b4](https://huggingface.co/nvidia/mit-b4) on the Hasano20/Clean_Set1_95images dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0223
- Mean Iou: 0.6447
- Mean Accuracy: 0.9824
- Overall Accuracy: 0.9886
- Accuracy Background: nan
- Accuracy Melt: 0.9724
- Accuracy Substrate: 0.9923
- Iou Background: 0.0
- Iou Melt: 0.9458
- Iou Substrate: 0.9882
## 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Melt | Accuracy Substrate | Iou Background | Iou Melt | Iou Substrate |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:------------------:|:--------------:|:--------:|:-------------:|
| 0.2051 | 1.1765 | 20 | 0.3764 | 0.3339 | 0.5766 | 0.8354 | nan | 0.1639 | 0.9894 | 0.0 | 0.1612 | 0.8404 |
| 0.3486 | 2.3529 | 40 | 0.1932 | 0.4595 | 0.7687 | 0.8745 | nan | 0.6000 | 0.9375 | 0.0 | 0.4928 | 0.8858 |
| 0.0831 | 3.5294 | 60 | 0.2016 | 0.4101 | 0.6782 | 0.8792 | nan | 0.3576 | 0.9988 | 0.0 | 0.3570 | 0.8732 |
| 0.0809 | 4.7059 | 80 | 0.0763 | 0.5787 | 0.9243 | 0.9507 | nan | 0.8822 | 0.9664 | 0.0 | 0.7830 | 0.9531 |
| 0.0325 | 5.8824 | 100 | 0.0694 | 0.6028 | 0.9436 | 0.9618 | nan | 0.9146 | 0.9727 | 0.0 | 0.8479 | 0.9606 |
| 0.0279 | 7.0588 | 120 | 0.0460 | 0.6142 | 0.9520 | 0.9712 | nan | 0.9213 | 0.9826 | 0.0 | 0.8739 | 0.9686 |
| 0.0493 | 8.2353 | 140 | 0.0353 | 0.6297 | 0.9648 | 0.9802 | nan | 0.9404 | 0.9893 | 0.0 | 0.9092 | 0.9797 |
| 0.0286 | 9.4118 | 160 | 0.0366 | 0.6261 | 0.9643 | 0.9765 | nan | 0.9449 | 0.9837 | 0.0 | 0.8997 | 0.9787 |
| 0.0463 | 10.5882 | 180 | 0.0258 | 0.6425 | 0.9798 | 0.9879 | nan | 0.9669 | 0.9927 | 0.0 | 0.9414 | 0.9862 |
| 0.0145 | 11.7647 | 200 | 0.0302 | 0.6324 | 0.9652 | 0.9821 | nan | 0.9382 | 0.9922 | 0.0 | 0.9162 | 0.9810 |
| 0.0221 | 12.9412 | 220 | 0.0262 | 0.6379 | 0.9733 | 0.9850 | nan | 0.9547 | 0.9919 | 0.0 | 0.9289 | 0.9848 |
| 0.0109 | 14.1176 | 240 | 0.0236 | 0.6417 | 0.9764 | 0.9869 | nan | 0.9595 | 0.9932 | 0.0 | 0.9379 | 0.9871 |
| 0.0122 | 15.2941 | 260 | 0.0252 | 0.6407 | 0.9812 | 0.9866 | nan | 0.9725 | 0.9898 | 0.0 | 0.9358 | 0.9864 |
| 0.0101 | 16.4706 | 280 | 0.0239 | 0.6417 | 0.9799 | 0.9869 | nan | 0.9686 | 0.9911 | 0.0 | 0.9382 | 0.9870 |
| 0.0113 | 17.6471 | 300 | 0.0231 | 0.6425 | 0.9798 | 0.9874 | nan | 0.9675 | 0.9920 | 0.0 | 0.9399 | 0.9875 |
| 0.0086 | 18.8235 | 320 | 0.0225 | 0.6444 | 0.9826 | 0.9885 | nan | 0.9733 | 0.9919 | 0.0 | 0.9451 | 0.9882 |
| 0.0086 | 20.0 | 340 | 0.0223 | 0.6447 | 0.9824 | 0.9886 | nan | 0.9724 | 0.9923 | 0.0 | 0.9458 | 0.9882 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.0.1+cu117
- Datasets 2.19.2
- Tokenizers 0.19.1
|