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# test-carbonate-segmentation2

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the edwardhuang/carbonate-thin-sections dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3642
- Mean Iou: 0.2180
- Mean Accuracy: 0.3344
- Overall Accuracy: 0.6454
- Accuracy Micrite: nan
- Accuracy Cement: nan
- Accuracy Peloid/pellet/ooid: nan
- Accuracy Biotic: 0.6660
- Accuracy Scale bar: 0.0028
- Iou Micrite: 0.0
- Iou Cement: nan
- Iou Peloid/pellet/ooid: nan
- Iou Biotic: 0.6511
- Iou Scale bar: 0.0028

## 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 Micrite | Accuracy Cement | Accuracy Peloid/pellet/ooid | Accuracy Biotic | Accuracy Scale bar | Iou Micrite | Iou Cement | Iou Peloid/pellet/ooid | Iou Biotic | Iou Scale bar |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:----------------:|:---------------:|:---------------------------:|:---------------:|:------------------:|:-----------:|:----------:|:----------------------:|:----------:|:-------------:|
| 1.2003        | 2.22  | 20   | 1.5260          | 0.4834   | 0.7334        | 0.9835           | nan              | nan             | nan                         | 1.0             | 0.4669             | 0.0         | nan        | nan                    | 0.9834     | 0.4669        |
| 1.2006        | 4.44  | 40   | 0.8923          | 0.6346   | 0.9521        | 0.9498           | nan              | nan             | nan                         | 0.9497          | 0.9545             | 0.0         | nan        | nan                    | 0.9494     | 0.9545        |
| 1.4233        | 6.67  | 60   | 1.0240          | 0.4417   | 0.6716        | 0.9793           | nan              | nan             | nan                         | 0.9995          | 0.3438             | 0.0         | nan        | nan                    | 0.9814     | 0.3438        |
| 1.1735        | 8.89  | 80   | 0.7964          | 0.5230   | 0.7890        | 0.9437           | nan              | nan             | nan                         | 0.9539          | 0.6241             | 0.0         | nan        | nan                    | 0.9449     | 0.6241        |
| 1.0242        | 11.11 | 100  | 0.8747          | 0.5322   | 0.8038        | 0.9849           | nan              | nan             | nan                         | 0.9969          | 0.6108             | 0.0         | nan        | nan                    | 0.9859     | 0.6108        |
| 0.9161        | 13.33 | 120  | 0.9217          | 0.5133   | 0.7767        | 0.9831           | nan              | nan             | nan                         | 0.9967          | 0.5568             | 0.0         | nan        | nan                    | 0.9832     | 0.5568        |
| 0.8102        | 15.56 | 140  | 0.7069          | 0.5923   | 0.8907        | 0.9490           | nan              | nan             | nan                         | 0.9529          | 0.8286             | 0.0         | nan        | nan                    | 0.9484     | 0.8286        |
| 0.5436        | 17.78 | 160  | 0.5149          | 0.3206   | 0.4929        | 0.8806           | nan              | nan             | nan                         | 0.9062          | 0.0795             | 0.0         | nan        | nan                    | 0.8823     | 0.0795        |
| 0.8517        | 20.0  | 180  | 0.5646          | 0.3748   | 0.5719        | 0.9200           | nan              | nan             | nan                         | 0.9430          | 0.2008             | 0.0         | nan        | nan                    | 0.9236     | 0.2008        |
| 0.4532        | 22.22 | 200  | 0.6128          | 0.3133   | 0.4837        | 0.9188           | nan              | nan             | nan                         | 0.9475          | 0.0199             | 0.0         | nan        | nan                    | 0.9201     | 0.0199        |
| 1.3133        | 24.44 | 220  | 0.3006          | 0.2391   | 0.3645        | 0.7064           | nan              | nan             | nan                         | 0.7290          | 0.0                | 0.0         | nan        | nan                    | 0.7172     | 0.0           |
| 0.4636        | 26.67 | 240  | 0.3260          | 0.1903   | 0.2901        | 0.5259           | nan              | nan             | nan                         | 0.5414          | 0.0388             | 0.0         | nan        | nan                    | 0.5320     | 0.0388        |
| 0.9843        | 28.89 | 260  | 0.3663          | 0.2741   | 0.4182        | 0.6986           | nan              | nan             | nan                         | 0.7171          | 0.1193             | 0.0         | nan        | nan                    | 0.7031     | 0.1193        |
| 0.7617        | 31.11 | 280  | 0.3338          | 0.2357   | 0.3627        | 0.7030           | nan              | nan             | nan                         | 0.7255          | 0.0                | 0.0         | nan        | nan                    | 0.7072     | 0.0           |
| 1.283         | 33.33 | 300  | 0.3395          | 0.2723   | 0.4176        | 0.7232           | nan              | nan             | nan                         | 0.7434          | 0.0919             | 0.0         | nan        | nan                    | 0.7250     | 0.0919        |
| 0.6578        | 35.56 | 320  | 0.3382          | 0.2069   | 0.3170        | 0.6143           | nan              | nan             | nan                         | 0.6339          | 0.0                | 0.0         | nan        | nan                    | 0.6207     | 0.0           |
| 0.2129        | 37.78 | 340  | 0.3436          | 0.2288   | 0.3525        | 0.6831           | nan              | nan             | nan                         | 0.7049          | 0.0                | 0.0         | nan        | nan                    | 0.6863     | 0.0           |
| 0.7001        | 40.0  | 360  | 0.2998          | 0.2001   | 0.3069        | 0.5771           | nan              | nan             | nan                         | 0.5950          | 0.0189             | 0.0         | nan        | nan                    | 0.5813     | 0.0189        |
| 0.3866        | 42.22 | 380  | 0.3162          | 0.1840   | 0.2819        | 0.5464           | nan              | nan             | nan                         | 0.5639          | 0.0                | 0.0         | nan        | nan                    | 0.5521     | 0.0           |
| 1.2623        | 44.44 | 400  | 0.3431          | 0.2125   | 0.3254        | 0.6172           | nan              | nan             | nan                         | 0.6365          | 0.0142             | 0.0         | nan        | nan                    | 0.6234     | 0.0142        |
| 0.6115        | 46.67 | 420  | 0.2987          | 0.2020   | 0.3095        | 0.5998           | nan              | nan             | nan                         | 0.6190          | 0.0                | 0.0         | nan        | nan                    | 0.6060     | 0.0           |
| 0.5802        | 48.89 | 440  | 0.3642          | 0.2180   | 0.3344        | 0.6454           | nan              | nan             | nan                         | 0.6660          | 0.0028             | 0.0         | nan        | nan                    | 0.6511     | 0.0028        |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Tokenizers 0.13.3

{"license": "other", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "model-index": [{"name": "test-carbonate-segmentation2", "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 

test-carbonate-segmentation2
============================


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


* Loss: 0.3642
* Mean Iou: 0.2180
* Mean Accuracy: 0.3344
* Overall Accuracy: 0.6454
* Accuracy Micrite: nan
* Accuracy Cement: nan
* Accuracy Peloid/pellet/ooid: nan
* Accuracy Biotic: 0.6660
* Accuracy Scale bar: 0.0028
* Iou Micrite: 0.0
* Iou Cement: nan
* Iou Peloid/pellet/ooid: nan
* Iou Biotic: 0.6511
* Iou Scale bar: 0.0028


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 4.28.0
* Pytorch 2.0.1+cu118
* Tokenizers 0.13.3



### 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 4.28.0
* Pytorch 2.0.1+cu118
* Tokenizers 0.13.3
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 4.28.0
* Pytorch 2.0.1+cu118
* Tokenizers 0.13.3