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# segformer-finetuned-lane-10k-steps

This model is a fine-tuned version of [nvidia/segformer-b0-finetuned-cityscapes-512-1024](https://huggingface.co/nvidia/segformer-b0-finetuned-cityscapes-512-1024) on the Efferbach/lane_master dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0365
- Mean Iou: 0.4899
- Mean Accuracy: 0.7371
- Overall Accuracy: 0.7371
- Accuracy Background: nan
- Accuracy Left: 0.7394
- Accuracy Right: 0.7348
- Iou Background: 0.0
- Iou Left: 0.7371
- Iou Right: 0.7325

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: polynomial
- training_steps: 10000

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Left | Accuracy Right | Iou Background | Iou Left | Iou Right |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:--------------:|:--------------:|:--------:|:---------:|
| 0.0792        | 1.0   | 308   | 0.0714          | 0.0148   | 0.0229        | 0.0225           | nan                 | 0.0373        | 0.0085         | 0.0            | 0.0362   | 0.0083    |
| 0.0437        | 2.0   | 616   | 0.0502          | 0.1687   | 0.2775        | 0.2784           | nan                 | 0.2492        | 0.3058         | 0.0            | 0.2343   | 0.2718    |
| 0.0326        | 3.0   | 924   | 0.0445          | 0.2614   | 0.4441        | 0.4479           | nan                 | 0.3134        | 0.5748         | 0.0            | 0.3100   | 0.4742    |
| 0.0224        | 4.0   | 1232  | 0.0370          | 0.4048   | 0.6098        | 0.6100           | nan                 | 0.6043        | 0.6153         | 0.0            | 0.6031   | 0.6113    |
| 0.0184        | 5.0   | 1540  | 0.0346          | 0.3820   | 0.5858        | 0.5870           | nan                 | 0.5421        | 0.6295         | 0.0            | 0.5400   | 0.6060    |
| 0.0159        | 6.0   | 1848  | 0.0319          | 0.4367   | 0.6567        | 0.6573           | nan                 | 0.6343        | 0.6791         | 0.0            | 0.6341   | 0.6760    |
| 0.0139        | 7.0   | 2156  | 0.0317          | 0.4555   | 0.6855        | 0.6860           | nan                 | 0.6691        | 0.7019         | 0.0            | 0.6680   | 0.6986    |
| 0.0129        | 8.0   | 2464  | 0.0321          | 0.4348   | 0.6533        | 0.6535           | nan                 | 0.6479        | 0.6588         | 0.0            | 0.6474   | 0.6571    |
| 0.0122        | 9.0   | 2772  | 0.0275          | 0.4541   | 0.6827        | 0.6830           | nan                 | 0.6710        | 0.6943         | 0.0            | 0.6697   | 0.6927    |
| 0.0111        | 10.0  | 3080  | 0.0305          | 0.4609   | 0.6928        | 0.6927           | nan                 | 0.6969        | 0.6887         | 0.0            | 0.6963   | 0.6865    |
| 0.011         | 11.0  | 3388  | 0.0286          | 0.4646   | 0.6988        | 0.6991           | nan                 | 0.6890        | 0.7087         | 0.0            | 0.6883   | 0.7055    |
| 0.0103        | 12.0  | 3696  | 0.0298          | 0.4693   | 0.7058        | 0.7062           | nan                 | 0.6939        | 0.7177         | 0.0            | 0.6932   | 0.7148    |
| 0.0097        | 13.0  | 4004  | 0.0293          | 0.4717   | 0.7090        | 0.7087           | nan                 | 0.7184        | 0.6996         | 0.0            | 0.7176   | 0.6975    |
| 0.0093        | 14.0  | 4312  | 0.0330          | 0.4537   | 0.6835        | 0.6836           | nan                 | 0.6775        | 0.6894         | 0.0            | 0.6768   | 0.6843    |
| 0.009         | 15.0  | 4620  | 0.0331          | 0.4804   | 0.7226        | 0.7226           | nan                 | 0.7194        | 0.7257         | 0.0            | 0.7178   | 0.7234    |
| 0.0088        | 16.0  | 4928  | 0.0315          | 0.4890   | 0.7355        | 0.7357           | nan                 | 0.7275        | 0.7435         | 0.0            | 0.7259   | 0.7411    |
| 0.0086        | 17.0  | 5236  | 0.0338          | 0.4813   | 0.7234        | 0.7234           | nan                 | 0.7224        | 0.7243         | 0.0            | 0.7216   | 0.7223    |
| 0.0085        | 18.0  | 5544  | 0.0348          | 0.4743   | 0.7129        | 0.7126           | nan                 | 0.7225        | 0.7033         | 0.0            | 0.7217   | 0.7012    |
| 0.0083        | 19.0  | 5852  | 0.0357          | 0.4812   | 0.7245        | 0.7244           | nan                 | 0.7281        | 0.7210         | 0.0            | 0.7254   | 0.7183    |
| 0.0081        | 20.0  | 6160  | 0.0334          | 0.4829   | 0.7271        | 0.7269           | nan                 | 0.7337        | 0.7205         | 0.0            | 0.7305   | 0.7182    |
| 0.0079        | 21.0  | 6468  | 0.0359          | 0.4773   | 0.7177        | 0.7177           | nan                 | 0.7184        | 0.7170         | 0.0            | 0.7174   | 0.7146    |
| 0.0077        | 22.0  | 6776  | 0.0351          | 0.4874   | 0.7332        | 0.7329           | nan                 | 0.7440        | 0.7223         | 0.0            | 0.7432   | 0.7190    |
| 0.0075        | 23.0  | 7084  | 0.0344          | 0.4855   | 0.7296        | 0.7292           | nan                 | 0.7437        | 0.7156         | 0.0            | 0.7425   | 0.7141    |
| 0.0077        | 24.0  | 7392  | 0.0362          | 0.4799   | 0.7216        | 0.7216           | nan                 | 0.7236        | 0.7196         | 0.0            | 0.7223   | 0.7174    |
| 0.0071        | 25.0  | 7700  | 0.0391          | 0.4775   | 0.7179        | 0.7180           | nan                 | 0.7173        | 0.7186         | 0.0            | 0.7161   | 0.7163    |
| 0.0077        | 26.0  | 8008  | 0.0339          | 0.4895   | 0.7367        | 0.7366           | nan                 | 0.7405        | 0.7329         | 0.0            | 0.7388   | 0.7297    |
| 0.0069        | 27.0  | 8316  | 0.0344          | 0.4858   | 0.7305        | 0.7305           | nan                 | 0.7291        | 0.7318         | 0.0            | 0.7278   | 0.7297    |
| 0.0069        | 28.0  | 8624  | 0.0361          | 0.4844   | 0.7283        | 0.7282           | nan                 | 0.7324        | 0.7243         | 0.0            | 0.7309   | 0.7221    |
| 0.007         | 29.0  | 8932  | 0.0371          | 0.4837   | 0.7273        | 0.7270           | nan                 | 0.7360        | 0.7186         | 0.0            | 0.7345   | 0.7166    |
| 0.007         | 30.0  | 9240  | 0.0366          | 0.4854   | 0.7305        | 0.7303           | nan                 | 0.7379        | 0.7231         | 0.0            | 0.7353   | 0.7208    |
| 0.0067        | 31.0  | 9548  | 0.0367          | 0.4866   | 0.7322        | 0.7321           | nan                 | 0.7357        | 0.7286         | 0.0            | 0.7335   | 0.7263    |
| 0.0068        | 32.0  | 9856  | 0.0364          | 0.4883   | 0.7348        | 0.7347           | nan                 | 0.7377        | 0.7318         | 0.0            | 0.7355   | 0.7295    |
| 0.0067        | 32.47 | 10000 | 0.0365          | 0.4899   | 0.7371        | 0.7371           | nan                 | 0.7394        | 0.7348         | 0.0            | 0.7371   | 0.7325    |


### Framework versions

- Transformers 4.28.0.dev0
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "other", "tags": ["image-segmentation", "vision", "generated_from_trainer"], "model-index": [{"name": "segformer-finetuned-lane-10k-steps", "results": []}]}
transformers pytorch tensorboard segformer image-segmentation vision generated_from_trainer license:other endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #segformer #image-segmentation #vision #generated_from_trainer #license-other #endpoints_compatible #region-us 

segformer-finetuned-lane-10k-steps
==================================


This model is a fine-tuned version of nvidia/segformer-b0-finetuned-cityscapes-512-1024 on the Efferbach/lane\_master dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0365
* Mean Iou: 0.4899
* Mean Accuracy: 0.7371
* Overall Accuracy: 0.7371
* Accuracy Background: nan
* Accuracy Left: 0.7394
* Accuracy Right: 0.7348
* Iou Background: 0.0
* Iou Left: 0.7371
* Iou Right: 0.7325


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: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: polynomial
* training\_steps: 10000


### Training results



### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 6e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 1337
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: polynomial
* training\_steps: 10000 ### Training results ### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #segformer #image-segmentation #vision #generated_from_trainer #license-other #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 6e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 1337
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: polynomial
* training\_steps: 10000 ### Training results ### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #segformer #image-segmentation #vision #generated_from_trainer #license-other #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 6e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 1337
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: polynomial
* training\_steps: 10000### Training results### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3