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# mobilenet_v2_1-10k-steps

This model is a fine-tuned version of [google/mobilenet_v2_1.0_224](https://huggingface.co/google/mobilenet_v2_1.0_224) on the Efferbach/lane_master2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0827
- Mean Iou: 0.0
- Mean Accuracy: 0.0
- Overall Accuracy: 0.0
- Accuracy Background: nan
- Accuracy Left: 0.0
- Accuracy Right: 0.0
- Iou Background: 0.0
- Iou Left: 0.0
- Iou Right: 0.0

## 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: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: polynomial
- training_steps: 10000

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Left | Accuracy Right | Iou Background | Iou Left | Iou Right |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:--------------:|:--------------:|:--------:|:---------:|
| 0.3253        | 1.0   | 385   | 0.0989          | 0.0      | 0.0           | 0.0              | nan                 | 0.0           | 0.0            | 0.0            | 0.0      | 0.0       |
| 0.128         | 2.0   | 770   | 0.1518          | 0.0      | 0.0           | 0.0              | nan                 | 0.0           | 0.0            | 0.0            | 0.0      | 0.0       |
| 0.1212        | 3.0   | 1155  | 0.1852          | 0.0      | 0.0           | 0.0              | nan                 | 0.0           | 0.0            | 0.0            | 0.0      | 0.0       |
| 0.117         | 4.0   | 1540  | 0.1446          | 0.0      | 0.0           | 0.0              | nan                 | 0.0           | 0.0            | 0.0            | 0.0      | 0.0       |
| 0.1148        | 5.0   | 1925  | 0.1087          | 0.0      | 0.0           | 0.0              | nan                 | 0.0           | 0.0            | 0.0            | 0.0      | 0.0       |
| 0.1167        | 6.0   | 2310  | 0.1502          | 0.0      | 0.0           | 0.0              | nan                 | 0.0           | 0.0            | 0.0            | 0.0      | 0.0       |
| 0.1128        | 7.0   | 2695  | 0.0882          | 0.0      | 0.0           | 0.0              | nan                 | 0.0           | 0.0            | 0.0            | 0.0      | 0.0       |
| 0.1156        | 8.0   | 3080  | 0.1005          | 0.0      | 0.0           | 0.0              | nan                 | 0.0           | 0.0            | 0.0            | 0.0      | 0.0       |
| 0.1164        | 9.0   | 3465  | 0.0844          | 0.0      | 0.0           | 0.0              | nan                 | 0.0           | 0.0            | 0.0            | 0.0      | 0.0       |
| 0.1128        | 10.0  | 3850  | 0.1497          | 0.0      | 0.0           | 0.0              | nan                 | 0.0           | 0.0            | 0.0            | 0.0      | 0.0       |
| 0.1151        | 11.0  | 4235  | 0.1024          | 0.0      | 0.0           | 0.0              | nan                 | 0.0           | 0.0            | 0.0            | 0.0      | 0.0       |
| 0.1112        | 12.0  | 4620  | 0.0869          | 0.0      | 0.0           | 0.0              | nan                 | 0.0           | 0.0            | 0.0            | 0.0      | 0.0       |
| 0.1093        | 13.0  | 5005  | 0.0940          | 0.0      | 0.0           | 0.0              | nan                 | 0.0           | 0.0            | 0.0            | 0.0      | 0.0       |
| 0.1102        | 14.0  | 5390  | 0.0914          | 0.0      | 0.0           | 0.0              | nan                 | 0.0           | 0.0            | 0.0            | 0.0      | 0.0       |
| 0.1111        | 15.0  | 5775  | 0.1047          | 0.0      | 0.0           | 0.0              | nan                 | 0.0           | 0.0            | 0.0            | 0.0      | 0.0       |
| 0.1087        | 16.0  | 6160  | 0.1104          | 0.0      | 0.0           | 0.0              | nan                 | 0.0           | 0.0            | 0.0            | 0.0      | 0.0       |
| 0.1105        | 17.0  | 6545  | 0.0970          | 0.0      | 0.0           | 0.0              | nan                 | 0.0           | 0.0            | 0.0            | 0.0      | 0.0       |
| 0.1083        | 18.0  | 6930  | 0.0868          | 0.0      | 0.0           | 0.0              | nan                 | 0.0           | 0.0            | 0.0            | 0.0      | 0.0       |
| 0.1077        | 19.0  | 7315  | 0.1121          | 0.0      | 0.0           | 0.0              | nan                 | 0.0           | 0.0            | 0.0            | 0.0      | 0.0       |
| 0.1115        | 20.0  | 7700  | 0.2092          | 0.0      | 0.0           | 0.0              | nan                 | 0.0           | 0.0            | 0.0            | 0.0      | 0.0       |
| 0.1102        | 21.0  | 8085  | 0.0850          | 0.0      | 0.0           | 0.0              | nan                 | 0.0           | 0.0            | 0.0            | 0.0      | 0.0       |
| 0.1077        | 22.0  | 8470  | 0.1011          | 0.0      | 0.0           | 0.0              | nan                 | 0.0           | 0.0            | 0.0            | 0.0      | 0.0       |
| 0.1111        | 23.0  | 8855  | 0.1136          | 0.0      | 0.0           | 0.0              | nan                 | 0.0           | 0.0            | 0.0            | 0.0      | 0.0       |
| 0.1099        | 24.0  | 9240  | 0.1001          | 0.0      | 0.0           | 0.0              | nan                 | 0.0           | 0.0            | 0.0            | 0.0      | 0.0       |
| 0.1086        | 25.0  | 9625  | 0.0997          | 0.0      | 0.0           | 0.0              | nan                 | 0.0           | 0.0            | 0.0            | 0.0      | 0.0       |
| 0.1066        | 25.97 | 10000 | 0.0827          | 0.0      | 0.0           | 0.0              | nan                 | 0.0           | 0.0            | 0.0            | 0.0      | 0.0       |


### Framework versions

- Transformers 4.28.0.dev0
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "other", "tags": ["image-segmentation", "vision", "generated_from_trainer"], "model-index": [{"name": "mobilenet_v2_1-10k-steps", "results": []}]}
transformers pytorch tensorboard mobilenet_v2 image-segmentation vision generated_from_trainer license:other endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #mobilenet_v2 #image-segmentation #vision #generated_from_trainer #license-other #endpoints_compatible #region-us 

mobilenet\_v2\_1-10k-steps
==========================


This model is a fine-tuned version of google/mobilenet\_v2\_1.0\_224 on the Efferbach/lane\_master2 dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0827
* Mean Iou: 0.0
* Mean Accuracy: 0.0
* Overall Accuracy: 0.0
* Accuracy Background: nan
* Accuracy Left: 0.0
* Accuracy Right: 0.0
* Iou Background: 0.0
* Iou Left: 0.0
* Iou Right: 0.0


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: 8
* eval\_batch\_size: 8
* seed: 1337
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: polynomial
* training\_steps: 10000


### Training results



### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 6e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 1337
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: polynomial
* training\_steps: 10000 ### Training results ### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #mobilenet_v2 #image-segmentation #vision #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: 8
* eval\_batch\_size: 8
* seed: 1337
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: polynomial
* training\_steps: 10000 ### Training results ### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #mobilenet_v2 #image-segmentation #vision #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: 8
* eval\_batch\_size: 8
* seed: 1337
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: polynomial
* training\_steps: 10000### Training results### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3