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# segformer-b1-solarModuleAnomaly-v0.1

This model is a fine-tuned version of [nvidia/mit-b1](https://huggingface.co/nvidia/mit-b1) on the zklee98/solarModuleAnomaly dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1547
- Mean Iou: 0.3822
- Mean Accuracy: 0.7643
- Overall Accuracy: 0.7643
- Accuracy Unlabelled: nan
- Accuracy Anomaly: 0.7643
- Iou Unlabelled: 0.0
- Iou Anomaly: 0.7643

## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabelled | Accuracy Anomaly | Iou Unlabelled | Iou Anomaly |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:----------------:|:--------------:|:-----------:|
| 0.4699        | 0.4   | 20   | 0.6337          | 0.4581   | 0.9162        | 0.9162           | nan                 | 0.9162           | 0.0            | 0.9162      |
| 0.3129        | 0.8   | 40   | 0.4636          | 0.3704   | 0.7407        | 0.7407           | nan                 | 0.7407           | 0.0            | 0.7407      |
| 0.2732        | 1.2   | 60   | 0.3164          | 0.3867   | 0.7734        | 0.7734           | nan                 | 0.7734           | 0.0            | 0.7734      |
| 0.2653        | 1.6   | 80   | 0.3769          | 0.4090   | 0.8180        | 0.8180           | nan                 | 0.8180           | 0.0            | 0.8180      |
| 0.2232        | 2.0   | 100  | 0.2976          | 0.2479   | 0.4958        | 0.4958           | nan                 | 0.4958           | 0.0            | 0.4958      |
| 0.5305        | 2.4   | 120  | 0.3151          | 0.3807   | 0.7613        | 0.7613           | nan                 | 0.7613           | 0.0            | 0.7613      |
| 0.2423        | 2.8   | 140  | 0.3189          | 0.4152   | 0.8305        | 0.8305           | nan                 | 0.8305           | 0.0            | 0.8305      |
| 0.3341        | 3.2   | 160  | 0.2384          | 0.3861   | 0.7723        | 0.7723           | nan                 | 0.7723           | 0.0            | 0.7723      |
| 0.2146        | 3.6   | 180  | 0.3200          | 0.4621   | 0.9243        | 0.9243           | nan                 | 0.9243           | 0.0            | 0.9243      |
| 0.1866        | 4.0   | 200  | 0.2510          | 0.3646   | 0.7291        | 0.7291           | nan                 | 0.7291           | 0.0            | 0.7291      |
| 0.2861        | 4.4   | 220  | 0.2736          | 0.4202   | 0.8404        | 0.8404           | nan                 | 0.8404           | 0.0            | 0.8404      |
| 0.2048        | 4.8   | 240  | 0.2410          | 0.3912   | 0.7823        | 0.7823           | nan                 | 0.7823           | 0.0            | 0.7823      |
| 0.1604        | 5.2   | 260  | 0.2233          | 0.3672   | 0.7344        | 0.7344           | nan                 | 0.7344           | 0.0            | 0.7344      |
| 0.2756        | 5.6   | 280  | 0.2705          | 0.4494   | 0.8987        | 0.8987           | nan                 | 0.8987           | 0.0            | 0.8987      |
| 0.1859        | 6.0   | 300  | 0.2211          | 0.4045   | 0.8089        | 0.8089           | nan                 | 0.8089           | 0.0            | 0.8089      |
| 0.1306        | 6.4   | 320  | 0.2140          | 0.3763   | 0.7525        | 0.7525           | nan                 | 0.7525           | 0.0            | 0.7525      |
| 0.5508        | 6.8   | 340  | 0.2231          | 0.4185   | 0.8371        | 0.8371           | nan                 | 0.8371           | 0.0            | 0.8371      |
| 0.1446        | 7.2   | 360  | 0.2139          | 0.3666   | 0.7332        | 0.7332           | nan                 | 0.7332           | 0.0            | 0.7332      |
| 0.3275        | 7.6   | 380  | 0.2470          | 0.3964   | 0.7928        | 0.7928           | nan                 | 0.7928           | 0.0            | 0.7928      |
| 0.164         | 8.0   | 400  | 0.2017          | 0.3910   | 0.7819        | 0.7819           | nan                 | 0.7819           | 0.0            | 0.7819      |
| 0.1864        | 8.4   | 420  | 0.2307          | 0.4408   | 0.8816        | 0.8816           | nan                 | 0.8816           | 0.0            | 0.8816      |
| 0.1578        | 8.8   | 440  | 0.1869          | 0.3707   | 0.7414        | 0.7414           | nan                 | 0.7414           | 0.0            | 0.7414      |
| 0.1201        | 9.2   | 460  | 0.2115          | 0.3834   | 0.7667        | 0.7667           | nan                 | 0.7667           | 0.0            | 0.7667      |
| 0.1783        | 9.6   | 480  | 0.2009          | 0.3747   | 0.7495        | 0.7495           | nan                 | 0.7495           | 0.0            | 0.7495      |
| 0.1232        | 10.0  | 500  | 0.1797          | 0.3865   | 0.7729        | 0.7729           | nan                 | 0.7729           | 0.0            | 0.7729      |
| 0.2572        | 10.4  | 520  | 0.1983          | 0.4057   | 0.8115        | 0.8115           | nan                 | 0.8115           | 0.0            | 0.8115      |
| 0.1209        | 10.8  | 540  | 0.1607          | 0.4274   | 0.8547        | 0.8547           | nan                 | 0.8547           | 0.0            | 0.8547      |
| 0.1234        | 11.2  | 560  | 0.2260          | 0.4066   | 0.8133        | 0.8133           | nan                 | 0.8133           | 0.0            | 0.8133      |
| 0.145         | 11.6  | 580  | 0.1963          | 0.3939   | 0.7878        | 0.7878           | nan                 | 0.7878           | 0.0            | 0.7878      |
| 0.0665        | 12.0  | 600  | 0.1912          | 0.3873   | 0.7747        | 0.7747           | nan                 | 0.7747           | 0.0            | 0.7747      |
| 0.0826        | 12.4  | 620  | 0.2095          | 0.4186   | 0.8373        | 0.8373           | nan                 | 0.8373           | 0.0            | 0.8373      |
| 0.1212        | 12.8  | 640  | 0.1732          | 0.4059   | 0.8118        | 0.8118           | nan                 | 0.8118           | 0.0            | 0.8118      |
| 0.142         | 13.2  | 660  | 0.2086          | 0.4007   | 0.8013        | 0.8013           | nan                 | 0.8013           | 0.0            | 0.8013      |
| 0.0899        | 13.6  | 680  | 0.1838          | 0.3928   | 0.7856        | 0.7856           | nan                 | 0.7856           | 0.0            | 0.7856      |
| 0.1857        | 14.0  | 700  | 0.1638          | 0.4157   | 0.8315        | 0.8315           | nan                 | 0.8315           | 0.0            | 0.8315      |
| 0.0788        | 14.4  | 720  | 0.1736          | 0.4112   | 0.8223        | 0.8223           | nan                 | 0.8223           | 0.0            | 0.8223      |
| 0.2543        | 14.8  | 740  | 0.1547          | 0.3822   | 0.7643        | 0.7643           | nan                 | 0.7643           | 0.0            | 0.7643      |


### Framework versions

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

{"license": "other", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "model-index": [{"name": "segformer-b1-solarModuleAnomaly-v0.1", "results": []}]}
transformers pytorch safetensors segformer vision image-segmentation generated_from_trainer license:other endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #safetensors #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #has_space #region-us 

segformer-b1-solarModuleAnomaly-v0.1
====================================


This model is a fine-tuned version of nvidia/mit-b1 on the zklee98/solarModuleAnomaly dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1547
* Mean Iou: 0.3822
* Mean Accuracy: 0.7643
* Overall Accuracy: 0.7643
* Accuracy Unlabelled: nan
* Accuracy Anomaly: 0.7643
* Iou Unlabelled: 0.0
* Iou Anomaly: 0.7643


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: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15


### Training results



### Framework versions


* Transformers 4.28.0
* 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: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15 ### Training results ### Framework versions


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


The following hyperparameters were used during training:


* learning\_rate: 6e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15 ### Training results ### Framework versions


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


The following hyperparameters were used during training:


* learning\_rate: 6e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15### Training results### Framework versions


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