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# segformer-b5-finetuned-magic-cards-230117-2

This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the andrewljohnson/magic_cards dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0491
- Mean Iou: 0.6649
- Mean Accuracy: 0.9974
- Overall Accuracy: 0.9972
- Accuracy Unlabeled: nan
- Accuracy Front: 0.9990
- Accuracy Back: 0.9957
- Iou Unlabeled: 0.0
- Iou Front: 0.9990
- Iou Back: 0.9957

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Front | Accuracy Back | Iou Unlabeled | Iou Front | Iou Back |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:--------------:|:-------------:|:-------------:|:---------:|:--------:|
| 0.5968        | 0.33  | 20   | 0.4422          | 0.6366   | 0.9701        | 0.9690           | nan                | 0.9812         | 0.9590        | 0.0           | 0.9507    | 0.9590   |
| 0.8955        | 0.66  | 40   | 0.2353          | 0.6496   | 0.9819        | 0.9807           | nan                | 0.9944         | 0.9695        | 0.0           | 0.9792    | 0.9695   |
| 0.1269        | 0.98  | 60   | 0.1739          | 0.6566   | 0.9922        | 0.9916           | nan                | 0.9979         | 0.9866        | 0.0           | 0.9832    | 0.9866   |
| 0.7629        | 1.31  | 80   | 0.1664          | 0.6561   | 0.9915        | 0.9909           | nan                | 0.9975         | 0.9856        | 0.0           | 0.9826    | 0.9856   |
| 0.106         | 1.64  | 100  | 0.1005          | 0.6641   | 0.9968        | 0.9967           | nan                | 0.9978         | 0.9959        | 0.0           | 0.9966    | 0.9959   |
| 0.3278        | 1.97  | 120  | 0.0577          | 0.6632   | 0.9948        | 0.9947           | nan                | 0.9963         | 0.9934        | 0.0           | 0.9963    | 0.9934   |
| 0.061         | 2.3   | 140  | 0.0655          | 0.6642   | 0.9963        | 0.9962           | nan                | 0.9972         | 0.9953        | 0.0           | 0.9972    | 0.9953   |
| 0.0766        | 2.62  | 160  | 0.0470          | 0.6635   | 0.9953        | 0.9954           | nan                | 0.9940         | 0.9966        | 0.0           | 0.9940    | 0.9966   |
| 0.0664        | 2.95  | 180  | 0.0436          | 0.6617   | 0.9926        | 0.9931           | nan                | 0.9877         | 0.9975        | 0.0           | 0.9877    | 0.9975   |
| 0.0655        | 3.28  | 200  | 0.0632          | 0.6649   | 0.9973        | 0.9971           | nan                | 0.9994         | 0.9953        | 0.0           | 0.9994    | 0.9953   |
| 0.0356        | 3.61  | 220  | 0.0755          | 0.6661   | 0.9991        | 0.9991           | nan                | 0.9992         | 0.9991        | 0.0           | 0.9992    | 0.9991   |
| 0.0516        | 3.93  | 240  | 0.0470          | 0.6643   | 0.9965        | 0.9963           | nan                | 0.9987         | 0.9943        | 0.0           | 0.9987    | 0.9943   |
| 0.0517        | 4.26  | 260  | 0.0481          | 0.6645   | 0.9967        | 0.9965           | nan                | 0.9989         | 0.9945        | 0.0           | 0.9989    | 0.9945   |
| 0.1886        | 4.59  | 280  | 0.0823          | 0.6659   | 0.9988        | 0.9987           | nan                | 0.9999         | 0.9977        | 0.0           | 0.9999    | 0.9977   |
| 0.0453        | 4.92  | 300  | 0.0491          | 0.6649   | 0.9974        | 0.9972           | nan                | 0.9990         | 0.9957        | 0.0           | 0.9990    | 0.9957   |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.8.0
- Tokenizers 0.13.0.dev0

{"license": "other", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "model-index": [{"name": "segformer-b5-finetuned-magic-cards-230117-2", "results": []}]}
transformers pytorch segformer vision image-segmentation generated_from_trainer license:other endpoints_compatible region:us
TAGS
#transformers #pytorch #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us 

segformer-b5-finetuned-magic-cards-230117-2
===========================================


This model is a fine-tuned version of nvidia/mit-b5 on the andrewljohnson/magic\_cards dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0491
* Mean Iou: 0.6649
* Mean Accuracy: 0.9974
* Overall Accuracy: 0.9972
* Accuracy Unlabeled: nan
* Accuracy Front: 0.9990
* Accuracy Back: 0.9957
* Iou Unlabeled: 0.0
* Iou Front: 0.9990
* Iou Back: 0.9957


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: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5


### Training results



### Framework versions


* Transformers 4.25.1
* Pytorch 1.12.1
* Datasets 2.8.0
* Tokenizers 0.13.0.dev0



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 6e-05
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.12.1
* Datasets 2.8.0
* Tokenizers 0.13.0.dev0
TAGS
#transformers #pytorch #segformer #vision #image-segmentation #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: 1
* eval\_batch\_size: 1
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.12.1
* Datasets 2.8.0
* Tokenizers 0.13.0.dev0
TAGS
#transformers #pytorch #segformer #vision #image-segmentation #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: 1
* eval\_batch\_size: 1
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5### Training results### Framework versions


* Transformers 4.25.1
* Pytorch 1.12.1
* Datasets 2.8.0
* Tokenizers 0.13.0.dev0