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

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: 0.2096
- Mean Iou: 0.6629
- Mean Accuracy: 0.9944
- Overall Accuracy: 0.9944
- Accuracy Unlabeled: nan
- Accuracy Front: 0.9997
- Accuracy Back: 0.9891
- Iou Unlabeled: 0.0
- Iou Front: 0.9997
- Iou Back: 0.9891

## 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: 10

### 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:--------------:|:-------------:|:-------------:|:---------:|:--------:|
| 0.496         | 0.74  | 20   | 0.4441          | 0.6552   | 0.9838        | 0.9838           | nan                | 0.9786         | 0.9890        | 0.0           | 0.9786    | 0.9869   |
| 0.1693        | 1.48  | 40   | 0.4098          | 0.6597   | 0.9897        | 0.9897           | nan                | 0.9943         | 0.9851        | 0.0           | 0.9943    | 0.9849   |
| 0.1172        | 2.22  | 60   | 0.2734          | 0.6582   | 0.9874        | 0.9874           | nan                | 0.9977         | 0.9770        | 0.0           | 0.9977    | 0.9770   |
| 0.1335        | 2.96  | 80   | 0.2637          | 0.6609   | 0.9914        | 0.9914           | nan                | 0.9959         | 0.9869        | 0.0           | 0.9959    | 0.9869   |
| 0.0781        | 3.7   | 100  | 0.5178          | 0.6644   | 0.9966        | 0.9966           | nan                | 0.9998         | 0.9933        | 0.0           | 0.9998    | 0.9933   |
| 0.1302        | 4.44  | 120  | 0.2753          | 0.6652   | 0.9978        | 0.9978           | nan                | 0.9993         | 0.9962        | 0.0           | 0.9993    | 0.9962   |
| 0.0688        | 5.19  | 140  | 0.1458          | 0.6618   | 0.9926        | 0.9926           | nan                | 0.9950         | 0.9903        | 0.0           | 0.9950    | 0.9903   |
| 0.0866        | 5.93  | 160  | 0.1763          | 0.6636   | 0.9954        | 0.9954           | nan                | 0.9962         | 0.9946        | 0.0           | 0.9962    | 0.9946   |
| 0.0525        | 6.67  | 180  | 0.1812          | 0.6627   | 0.9941        | 0.9941           | nan                | 0.9988         | 0.9895        | 0.0           | 0.9988    | 0.9895   |
| 0.0679        | 7.41  | 200  | 0.2246          | 0.6625   | 0.9937        | 0.9937           | nan                | 0.9990         | 0.9884        | 0.0           | 0.9990    | 0.9884   |
| 0.0424        | 8.15  | 220  | 0.2079          | 0.6623   | 0.9934        | 0.9935           | nan                | 0.9996         | 0.9873        | 0.0           | 0.9996    | 0.9873   |
| 0.0349        | 8.89  | 240  | 0.1559          | 0.6626   | 0.9939        | 0.9940           | nan                | 0.9987         | 0.9892        | 0.0           | 0.9987    | 0.9892   |
| 0.0357        | 9.63  | 260  | 0.2096          | 0.6629   | 0.9944        | 0.9944           | nan                | 0.9997         | 0.9891        | 0.0           | 0.9997    | 0.9891   |


### 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", "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
=========================================


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: 0.2096
* Mean Iou: 0.6629
* Mean Accuracy: 0.9944
* Overall Accuracy: 0.9944
* Accuracy Unlabeled: nan
* Accuracy Front: 0.9997
* Accuracy Back: 0.9891
* Iou Unlabeled: 0.0
* Iou Front: 0.9997
* Iou Back: 0.9891


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: 10


### 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: 10 ### 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: 10 ### 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: 10### Training results### Framework versions


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