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# segformer-b0-finetuned-segments-gear2

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the marcomameli01/gear dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1268
- Mean Iou: 0.1254
- Mean Accuracy: 0.2509
- Overall Accuracy: 0.2509
- Per Category Iou: [0.0, 0.2508641975308642]
- Per Category Accuracy: [nan, 0.2508641975308642]

## 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 | Per Category Iou           | Per Category Accuracy      |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------------------:|:--------------------------:|
| 0.4614        | 5.0   | 20   | 0.4427          | 0.0741   | 0.1481        | 0.1481           | [0.0, 0.14814814814814814] | [nan, 0.14814814814814814] |
| 0.3327        | 10.0  | 40   | 0.2933          | 0.1726   | 0.3453        | 0.3453           | [0.0, 0.34528395061728395] | [nan, 0.34528395061728395] |
| 0.2305        | 15.0  | 60   | 0.2244          | 0.0382   | 0.0763        | 0.0763           | [0.0, 0.07634567901234568] | [nan, 0.07634567901234568] |
| 0.2011        | 20.0  | 80   | 0.2130          | 0.0374   | 0.0748        | 0.0748           | [0.0, 0.07476543209876543] | [nan, 0.07476543209876543] |
| 0.1846        | 25.0  | 100  | 0.1672          | 0.1037   | 0.2073        | 0.2073           | [0.0, 0.20730864197530866] | [nan, 0.20730864197530866] |
| 0.1622        | 30.0  | 120  | 0.1532          | 0.0805   | 0.1611        | 0.1611           | [0.0, 0.1610864197530864]  | [nan, 0.1610864197530864]  |
| 0.139         | 35.0  | 140  | 0.1396          | 0.0971   | 0.1942        | 0.1942           | [0.0, 0.19417283950617284] | [nan, 0.19417283950617284] |
| 0.1342        | 40.0  | 160  | 0.1283          | 0.0748   | 0.1496        | 0.1496           | [0.0, 0.14962962962962964] | [nan, 0.14962962962962964] |
| 0.128         | 45.0  | 180  | 0.1224          | 0.1128   | 0.2256        | 0.2256           | [0.0, 0.22558024691358025] | [nan, 0.22558024691358025] |
| 0.1243        | 50.0  | 200  | 0.1268          | 0.1254   | 0.2509        | 0.2509           | [0.0, 0.2508641975308642]  | [nan, 0.2508641975308642]  |


### Framework versions

- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["vision", "gear-segmentation", "generated_from_trainer"], "model-index": [{"name": "segformer-b0-finetuned-segments-gear2", "results": []}]}
transformers pytorch tensorboard segformer vision gear-segmentation generated_from_trainer license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #segformer #vision #gear-segmentation #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 

segformer-b0-finetuned-segments-gear2
=====================================


This model is a fine-tuned version of nvidia/mit-b0 on the marcomameli01/gear dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1268
* Mean Iou: 0.1254
* Mean Accuracy: 0.2509
* Overall Accuracy: 0.2509
* Per Category Iou: [0.0, 0.2508641975308642]
* Per Category Accuracy: [nan, 0.2508641975308642]


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 4.20.0
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1



### 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 4.20.0
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #segformer #vision #gear-segmentation #generated_from_trainer #license-apache-2.0 #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 4.20.0
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #segformer #vision #gear-segmentation #generated_from_trainer #license-apache-2.0 #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 4.20.0
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1