File size: 7,625 Bytes
93769c1 |
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 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 |
---
library_name: transformers
license: other
base_model: nvidia/mit-b0
tags:
- generated_from_trainer
model-index:
- name: segmentation_model_50ep
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. -->
# segmentation_model_50ep
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0063
- Mean Iou: 0.9981
- Mean Accuracy: 1.0
- Overall Accuracy: 1.0
- Per Category Iou: [0.9980539089681099]
- Per Category Accuracy: [1.0]
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------------:|:---------------------:|
| 0.049 | 1.2195 | 100 | 0.0429 | 0.9981 | 1.0 | 1.0 | [0.9980539089681099] | [1.0] |
| 0.029 | 2.4390 | 200 | 0.0274 | 0.9981 | 1.0 | 1.0 | [0.9980539089681099] | [1.0] |
| 0.0171 | 3.6585 | 300 | 0.0192 | 0.9981 | 1.0 | 1.0 | [0.9980539089681099] | [1.0] |
| 0.0158 | 4.8780 | 400 | 0.0187 | 0.9981 | 1.0 | 1.0 | [0.9980539089681099] | [1.0] |
| 0.019 | 6.0976 | 500 | 0.0169 | 0.9981 | 1.0 | 1.0 | [0.9980539089681099] | [1.0] |
| 0.013 | 7.3171 | 600 | 0.0125 | 0.9981 | 1.0 | 1.0 | [0.9980539089681099] | [1.0] |
| 0.0131 | 8.5366 | 700 | 0.0124 | 0.9981 | 1.0 | 1.0 | [0.9980539089681099] | [1.0] |
| 0.0111 | 9.7561 | 800 | 0.0101 | 0.9981 | 1.0 | 1.0 | [0.9980539089681099] | [1.0] |
| 0.0089 | 10.9756 | 900 | 0.0102 | 0.9981 | 1.0 | 1.0 | [0.9980539089681099] | [1.0] |
| 0.0106 | 12.1951 | 1000 | 0.0088 | 0.9981 | 1.0 | 1.0 | [0.9980539089681099] | [1.0] |
| 0.0093 | 13.4146 | 1100 | 0.0084 | 0.9981 | 1.0 | 1.0 | [0.9980539089681099] | [1.0] |
| 0.0088 | 14.6341 | 1200 | 0.0079 | 0.9981 | 1.0 | 1.0 | [0.9980539089681099] | [1.0] |
| 0.0084 | 15.8537 | 1300 | 0.0080 | 0.9981 | 1.0 | 1.0 | [0.9980539089681099] | [1.0] |
| 0.0089 | 17.0732 | 1400 | 0.0077 | 0.9981 | 1.0 | 1.0 | [0.9980539089681099] | [1.0] |
| 0.0087 | 18.2927 | 1500 | 0.0069 | 0.9981 | 1.0 | 1.0 | [0.9980539089681099] | [1.0] |
| 0.0072 | 19.5122 | 1600 | 0.0075 | 0.9981 | 1.0 | 1.0 | [0.9980539089681099] | [1.0] |
| 0.0087 | 20.7317 | 1700 | 0.0068 | 0.9981 | 1.0 | 1.0 | [0.9980539089681099] | [1.0] |
| 0.0094 | 21.9512 | 1800 | 0.0070 | 0.9981 | 1.0 | 1.0 | [0.9980539089681099] | [1.0] |
| 0.0074 | 23.1707 | 1900 | 0.0070 | 0.9981 | 1.0 | 1.0 | [0.9980539089681099] | [1.0] |
| 0.0075 | 24.3902 | 2000 | 0.0069 | 0.9981 | 1.0 | 1.0 | [0.9980539089681099] | [1.0] |
| 0.007 | 25.6098 | 2100 | 0.0064 | 0.9981 | 1.0 | 1.0 | [0.9980539089681099] | [1.0] |
| 0.0053 | 26.8293 | 2200 | 0.0065 | 0.9981 | 1.0 | 1.0 | [0.9980539089681099] | [1.0] |
| 0.0072 | 28.0488 | 2300 | 0.0063 | 0.9981 | 1.0 | 1.0 | [0.9980539089681099] | [1.0] |
| 0.0082 | 29.2683 | 2400 | 0.0065 | 0.9981 | 1.0 | 1.0 | [0.9980539089681099] | [1.0] |
| 0.0065 | 30.4878 | 2500 | 0.0066 | 0.9981 | 1.0 | 1.0 | [0.9980539089681099] | [1.0] |
| 0.0054 | 31.7073 | 2600 | 0.0065 | 0.9981 | 1.0 | 1.0 | [0.9980539089681099] | [1.0] |
| 0.0079 | 32.9268 | 2700 | 0.0066 | 0.9981 | 1.0 | 1.0 | [0.9980539089681099] | [1.0] |
| 0.006 | 34.1463 | 2800 | 0.0064 | 0.9981 | 1.0 | 1.0 | [0.9980539089681099] | [1.0] |
| 0.0053 | 35.3659 | 2900 | 0.0063 | 0.9981 | 1.0 | 1.0 | [0.9980539089681099] | [1.0] |
| 0.0059 | 36.5854 | 3000 | 0.0064 | 0.9981 | 1.0 | 1.0 | [0.9980539089681099] | [1.0] |
| 0.0061 | 37.8049 | 3100 | 0.0066 | 0.9981 | 1.0 | 1.0 | [0.9980539089681099] | [1.0] |
| 0.007 | 39.0244 | 3200 | 0.0064 | 0.9981 | 1.0 | 1.0 | [0.9980539089681099] | [1.0] |
| 0.0058 | 40.2439 | 3300 | 0.0063 | 0.9981 | 1.0 | 1.0 | [0.9980539089681099] | [1.0] |
| 0.0055 | 41.4634 | 3400 | 0.0062 | 0.9981 | 1.0 | 1.0 | [0.9980539089681099] | [1.0] |
| 0.0068 | 42.6829 | 3500 | 0.0064 | 0.9981 | 1.0 | 1.0 | [0.9980539089681099] | [1.0] |
| 0.0058 | 43.9024 | 3600 | 0.0063 | 0.9981 | 1.0 | 1.0 | [0.9980539089681099] | [1.0] |
| 0.0061 | 45.1220 | 3700 | 0.0064 | 0.9981 | 1.0 | 1.0 | [0.9980539089681099] | [1.0] |
| 0.003 | 46.3415 | 3800 | 0.0063 | 0.9981 | 1.0 | 1.0 | [0.9980539089681099] | [1.0] |
| 0.0058 | 47.5610 | 3900 | 0.0063 | 0.9981 | 1.0 | 1.0 | [0.9980539089681099] | [1.0] |
| 0.0087 | 48.7805 | 4000 | 0.0063 | 0.9981 | 1.0 | 1.0 | [0.9980539089681099] | [1.0] |
| 0.006 | 50.0 | 4100 | 0.0063 | 0.9981 | 1.0 | 1.0 | [0.9980539089681099] | [1.0] |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.2.0
- Datasets 2.4.0
- Tokenizers 0.20.3
|