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# segformer-b0-finetuned-segments-rowbody-4cats

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1486
- Mean Iou: 0.6527
- Mean Accuracy: 0.9381
- Overall Accuracy: 0.9558
- Accuracy Sleeve-right: nan
- Accuracy Sleeve-left: 0.9259
- Accuracy Neck: 0.9212
- Accuracy Body: 0.9670
- Iou Sleeve-right: 0.0
- Iou Sleeve-left: 0.9012
- Iou Neck: 0.7545
- Iou Body: 0.9551

## 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: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Sleeve-right | Accuracy Sleeve-left | Accuracy Neck | Accuracy Body | Iou Sleeve-right | Iou Sleeve-left | Iou Neck | Iou Body |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:---------------------:|:--------------------:|:-------------:|:-------------:|:----------------:|:---------------:|:--------:|:--------:|
| 0.9629        | 2.5   | 20   | 1.1228          | 0.2325   | 0.3900        | 0.7680           | nan                   | 0.1708               | 0.0           | 0.9991        | 0.0              | 0.1703          | 0.0      | 0.7598   |
| 0.6667        | 5.0   | 40   | 0.6465          | 0.4513   | 0.6646        | 0.8681           | nan                   | 0.5731               | 0.4337        | 0.9870        | 0.0              | 0.5659          | 0.3911   | 0.8483   |
| 0.414         | 7.5   | 60   | 0.4340          | 0.5935   | 0.8506        | 0.9377           | nan                   | 0.8496               | 0.7215        | 0.9806        | 0.0              | 0.8374          | 0.6136   | 0.9228   |
| 0.3351        | 10.0  | 80   | 0.3371          | 0.6263   | 0.9237        | 0.9411           | nan                   | 0.9325               | 0.8909        | 0.9478        | 0.0              | 0.8568          | 0.7171   | 0.9314   |
| 0.4849        | 12.5  | 100  | 0.3146          | 0.6428   | 0.9438        | 0.9526           | nan                   | 0.9708               | 0.9093        | 0.9513        | 0.0              | 0.8922          | 0.7375   | 0.9415   |
| 0.2461        | 15.0  | 120  | 0.2716          | 0.6431   | 0.9554        | 0.9496           | nan                   | 0.9717               | 0.9511        | 0.9434        | 0.0              | 0.8938          | 0.7410   | 0.9373   |
| 0.2123        | 17.5  | 140  | 0.2477          | 0.6715   | 0.9481        | 0.9679           | nan                   | 0.9523               | 0.9151        | 0.9768        | 0.0              | 0.9229          | 0.7999   | 0.9632   |
| 0.1827        | 20.0  | 160  | 0.2413          | 0.6620   | 0.9048        | 0.9649           | nan                   | 0.9200               | 0.8032        | 0.9912        | 0.0              | 0.9147          | 0.7717   | 0.9617   |
| 0.2828        | 22.5  | 180  | 0.2286          | 0.6484   | 0.9472        | 0.9532           | nan                   | 0.9622               | 0.9265        | 0.9530        | 0.0              | 0.8996          | 0.7495   | 0.9443   |
| 0.4631        | 25.0  | 200  | 0.2137          | 0.6459   | 0.9452        | 0.9485           | nan                   | 0.9523               | 0.9345        | 0.9486        | 0.0              | 0.8886          | 0.7543   | 0.9408   |
| 0.159         | 27.5  | 220  | 0.1854          | 0.6336   | 0.9374        | 0.9415           | nan                   | 0.9355               | 0.9328        | 0.9440        | 0.0              | 0.8832          | 0.7145   | 0.9368   |
| 0.1361        | 30.0  | 240  | 0.1760          | 0.6563   | 0.9429        | 0.9576           | nan                   | 0.9492               | 0.9161        | 0.9635        | 0.0              | 0.9027          | 0.7687   | 0.9538   |
| 0.1369        | 32.5  | 260  | 0.1634          | 0.6516   | 0.9451        | 0.9562           | nan                   | 0.9497               | 0.9249        | 0.9607        | 0.0              | 0.9130          | 0.7426   | 0.9507   |
| 0.1356        | 35.0  | 280  | 0.1580          | 0.6488   | 0.9248        | 0.9527           | nan                   | 0.9154               | 0.8905        | 0.9684        | 0.0              | 0.8966          | 0.7457   | 0.9528   |
| 0.2415        | 37.5  | 300  | 0.1635          | 0.6457   | 0.9439        | 0.9523           | nan                   | 0.9406               | 0.9340        | 0.9571        | 0.0              | 0.9003          | 0.7344   | 0.9482   |
| 0.1183        | 40.0  | 320  | 0.1639          | 0.6463   | 0.9386        | 0.9524           | nan                   | 0.9286               | 0.9262        | 0.9612        | 0.0              | 0.8998          | 0.7351   | 0.9500   |
| 0.1264        | 42.5  | 340  | 0.1564          | 0.6512   | 0.9439        | 0.9552           | nan                   | 0.9385               | 0.9312        | 0.9618        | 0.0              | 0.9066          | 0.7463   | 0.9521   |
| 0.2418        | 45.0  | 360  | 0.1553          | 0.6569   | 0.9366        | 0.9589           | nan                   | 0.9198               | 0.9166        | 0.9734        | 0.0              | 0.9033          | 0.7656   | 0.9587   |
| 0.189         | 47.5  | 380  | 0.1611          | 0.6520   | 0.9386        | 0.9544           | nan                   | 0.9284               | 0.9231        | 0.9643        | 0.0              | 0.8990          | 0.7554   | 0.9536   |
| 0.1132        | 50.0  | 400  | 0.1486          | 0.6527   | 0.9381        | 0.9558           | nan                   | 0.9259               | 0.9212        | 0.9670        | 0.0              | 0.9012          | 0.7545   | 0.9551   |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2

{"license": "other", "tags": ["generated_from_trainer"], "model-index": [{"name": "segformer-b0-finetuned-segments-rowbody-4cats", "results": []}]}
transformers pytorch tensorboard segformer generated_from_trainer license:other endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #segformer #generated_from_trainer #license-other #endpoints_compatible #region-us 

segformer-b0-finetuned-segments-rowbody-4cats
=============================================


This model is a fine-tuned version of nvidia/mit-b0 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1486
* Mean Iou: 0.6527
* Mean Accuracy: 0.9381
* Overall Accuracy: 0.9558
* Accuracy Sleeve-right: nan
* Accuracy Sleeve-left: 0.9259
* Accuracy Neck: 0.9212
* Accuracy Body: 0.9670
* Iou Sleeve-right: 0.0
* Iou Sleeve-left: 0.9012
* Iou Neck: 0.7545
* Iou Body: 0.9551


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: 50


### Training results



### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.0+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2



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


* Transformers 4.25.1
* Pytorch 1.13.0+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #segformer #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: 50 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.0+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #segformer #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: 50### Training results### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.0+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2