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# segformer-b4-finetuned-segments-torso

This model is a fine-tuned version of [nvidia/mit-b4](https://huggingface.co/nvidia/mit-b4) on the carllau999/torso_mask dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0823
- Mean Iou: 0.4815
- Mean Accuracy: 0.9630
- Overall Accuracy: 0.9630
- Accuracy Unlabeled: nan
- Accuracy Torso: 0.9630
- Iou Unlabeled: 0.0
- Iou Torso: 0.9630

## 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: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Torso | Iou Unlabeled | Iou Torso |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:--------------:|:-------------:|:---------:|
| 0.1599        | 1.0   | 20   | 0.1789          | 0.3774   | 0.7549        | 0.7549           | nan                | 0.7549         | 0.0           | 0.7549    |
| 0.0432        | 2.0   | 40   | 0.1167          | 0.4549   | 0.9099        | 0.9099           | nan                | 0.9099         | 0.0           | 0.9099    |
| 0.0329        | 3.0   | 60   | 0.1006          | 0.4489   | 0.8978        | 0.8978           | nan                | 0.8978         | 0.0           | 0.8978    |
| 0.0392        | 4.0   | 80   | 0.1742          | 0.4862   | 0.9724        | 0.9724           | nan                | 0.9724         | 0.0           | 0.9724    |
| 0.025         | 5.0   | 100  | 0.1047          | 0.4726   | 0.9452        | 0.9452           | nan                | 0.9452         | 0.0           | 0.9452    |
| 0.0229        | 6.0   | 120  | 0.1330          | 0.4771   | 0.9543        | 0.9543           | nan                | 0.9543         | 0.0           | 0.9543    |
| 0.0184        | 7.0   | 140  | 0.1060          | 0.4749   | 0.9498        | 0.9498           | nan                | 0.9498         | 0.0           | 0.9498    |
| 0.0117        | 8.0   | 160  | 0.1163          | 0.4719   | 0.9439        | 0.9439           | nan                | 0.9439         | 0.0           | 0.9439    |
| 0.0202        | 9.0   | 180  | 0.0790          | 0.4634   | 0.9268        | 0.9268           | nan                | 0.9268         | 0.0           | 0.9268    |
| 0.0124        | 10.0  | 200  | 0.1063          | 0.4767   | 0.9534        | 0.9534           | nan                | 0.9534         | 0.0           | 0.9534    |
| 0.0159        | 11.0  | 220  | 0.0979          | 0.4782   | 0.9563        | 0.9563           | nan                | 0.9563         | 0.0           | 0.9563    |
| 0.0093        | 12.0  | 240  | 0.0850          | 0.4746   | 0.9492        | 0.9492           | nan                | 0.9492         | 0.0           | 0.9492    |
| 0.0117        | 13.0  | 260  | 0.0870          | 0.4742   | 0.9484        | 0.9484           | nan                | 0.9484         | 0.0           | 0.9484    |
| 0.0087        | 14.0  | 280  | 0.1058          | 0.4809   | 0.9617        | 0.9617           | nan                | 0.9617         | 0.0           | 0.9617    |
| 0.0085        | 15.0  | 300  | 0.0897          | 0.4743   | 0.9485        | 0.9485           | nan                | 0.9485         | 0.0           | 0.9485    |
| 0.01          | 16.0  | 320  | 0.0813          | 0.4774   | 0.9547        | 0.9547           | nan                | 0.9547         | 0.0           | 0.9547    |
| 0.0109        | 17.0  | 340  | 0.0980          | 0.4810   | 0.9621        | 0.9621           | nan                | 0.9621         | 0.0           | 0.9621    |
| 0.0076        | 18.0  | 360  | 0.0923          | 0.4787   | 0.9574        | 0.9574           | nan                | 0.9574         | 0.0           | 0.9574    |
| 0.0067        | 19.0  | 380  | 0.0874          | 0.4778   | 0.9556        | 0.9556           | nan                | 0.9556         | 0.0           | 0.9556    |
| 0.0079        | 20.0  | 400  | 0.0821          | 0.4766   | 0.9533        | 0.9533           | nan                | 0.9533         | 0.0           | 0.9533    |
| 0.007         | 21.0  | 420  | 0.0876          | 0.4746   | 0.9491        | 0.9491           | nan                | 0.9491         | 0.0           | 0.9491    |
| 0.0091        | 22.0  | 440  | 0.0924          | 0.4695   | 0.9390        | 0.9390           | nan                | 0.9390         | 0.0           | 0.9390    |
| 0.0089        | 23.0  | 460  | 0.0711          | 0.4781   | 0.9563        | 0.9563           | nan                | 0.9563         | 0.0           | 0.9563    |
| 0.0067        | 24.0  | 480  | 0.0743          | 0.4759   | 0.9518        | 0.9518           | nan                | 0.9518         | 0.0           | 0.9518    |
| 0.0061        | 25.0  | 500  | 0.0868          | 0.4803   | 0.9605        | 0.9605           | nan                | 0.9605         | 0.0           | 0.9605    |
| 0.0053        | 26.0  | 520  | 0.0814          | 0.4800   | 0.9599        | 0.9599           | nan                | 0.9599         | 0.0           | 0.9599    |
| 0.0055        | 27.0  | 540  | 0.0779          | 0.4806   | 0.9612        | 0.9612           | nan                | 0.9612         | 0.0           | 0.9612    |
| 0.0066        | 28.0  | 560  | 0.0810          | 0.4814   | 0.9628        | 0.9628           | nan                | 0.9628         | 0.0           | 0.9628    |
| 0.0077        | 29.0  | 580  | 0.0814          | 0.4810   | 0.9621        | 0.9621           | nan                | 0.9621         | 0.0           | 0.9621    |
| 0.008         | 30.0  | 600  | 0.0852          | 0.4829   | 0.9658        | 0.9658           | nan                | 0.9658         | 0.0           | 0.9658    |
| 0.0062        | 31.0  | 620  | 0.0839          | 0.4833   | 0.9665        | 0.9665           | nan                | 0.9665         | 0.0           | 0.9665    |
| 0.0063        | 32.0  | 640  | 0.0778          | 0.4835   | 0.9671        | 0.9671           | nan                | 0.9671         | 0.0           | 0.9671    |
| 0.0055        | 33.0  | 660  | 0.0826          | 0.4816   | 0.9633        | 0.9633           | nan                | 0.9633         | 0.0           | 0.9633    |
| 0.0057        | 34.0  | 680  | 0.0831          | 0.4816   | 0.9632        | 0.9632           | nan                | 0.9632         | 0.0           | 0.9632    |
| 0.007         | 35.0  | 700  | 0.0809          | 0.4815   | 0.9630        | 0.9630           | nan                | 0.9630         | 0.0           | 0.9630    |
| 0.007         | 36.0  | 720  | 0.0776          | 0.4809   | 0.9618        | 0.9618           | nan                | 0.9618         | 0.0           | 0.9618    |
| 0.0075        | 37.0  | 740  | 0.0787          | 0.4809   | 0.9618        | 0.9618           | nan                | 0.9618         | 0.0           | 0.9618    |
| 0.0054        | 38.0  | 760  | 0.0838          | 0.4817   | 0.9633        | 0.9633           | nan                | 0.9633         | 0.0           | 0.9633    |
| 0.0061        | 39.0  | 780  | 0.0824          | 0.4820   | 0.9639        | 0.9639           | nan                | 0.9639         | 0.0           | 0.9639    |
| 0.0063        | 40.0  | 800  | 0.0799          | 0.4825   | 0.9650        | 0.9650           | nan                | 0.9650         | 0.0           | 0.9650    |
| 0.0065        | 41.0  | 820  | 0.0805          | 0.4813   | 0.9626        | 0.9626           | nan                | 0.9626         | 0.0           | 0.9626    |
| 0.0052        | 42.0  | 840  | 0.0838          | 0.4822   | 0.9644        | 0.9644           | nan                | 0.9644         | 0.0           | 0.9644    |
| 0.007         | 43.0  | 860  | 0.0841          | 0.4825   | 0.9650        | 0.9650           | nan                | 0.9650         | 0.0           | 0.9650    |
| 0.0054        | 44.0  | 880  | 0.0822          | 0.4817   | 0.9634        | 0.9634           | nan                | 0.9634         | 0.0           | 0.9634    |
| 0.0055        | 45.0  | 900  | 0.0815          | 0.4817   | 0.9633        | 0.9633           | nan                | 0.9633         | 0.0           | 0.9633    |
| 0.0084        | 46.0  | 920  | 0.0815          | 0.4819   | 0.9638        | 0.9638           | nan                | 0.9638         | 0.0           | 0.9638    |
| 0.005         | 47.0  | 940  | 0.0810          | 0.4814   | 0.9627        | 0.9627           | nan                | 0.9627         | 0.0           | 0.9627    |
| 0.0044        | 48.0  | 960  | 0.0822          | 0.4826   | 0.9652        | 0.9652           | nan                | 0.9652         | 0.0           | 0.9652    |
| 0.0053        | 49.0  | 980  | 0.0819          | 0.4821   | 0.9642        | 0.9642           | nan                | 0.9642         | 0.0           | 0.9642    |
| 0.0066        | 50.0  | 1000 | 0.0823          | 0.4815   | 0.9630        | 0.9630           | nan                | 0.9630         | 0.0           | 0.9630    |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cpu
- Datasets 2.11.0
- Tokenizers 0.13.2

{"license": "other", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "model-index": [{"name": "segformer-b4-finetuned-segments-torso", "results": []}]}
transformers pytorch tensorboard segformer vision image-segmentation generated_from_trainer license:other endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us 

segformer-b4-finetuned-segments-torso
=====================================


This model is a fine-tuned version of nvidia/mit-b4 on the carllau999/torso\_mask dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0823
* Mean Iou: 0.4815
* Mean Accuracy: 0.9630
* Overall Accuracy: 0.9630
* Accuracy Unlabeled: nan
* Accuracy Torso: 0.9630
* Iou Unlabeled: 0.0
* Iou Torso: 0.9630


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: 50


### Training results



### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cpu
* Datasets 2.11.0
* Tokenizers 0.13.2



### 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: 50 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cpu
* Datasets 2.11.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #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: 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: 50 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cpu
* Datasets 2.11.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #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: 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: 50### Training results### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cpu
* Datasets 2.11.0
* Tokenizers 0.13.2