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# segformer-b0-finetuned-segments-sidewalk-2

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the pixel_values, the label and the {'pixel_values': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1920x1080 at 0x7FCAFB662B60>, 'label': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=1x1 at 0x7FCAFB662B30>} datasets.
It achieves the following results on the evaluation set:
- Loss: 3.5116
- Mean Iou: 0.0268
- Mean Accuracy: 0.0661
- Overall Accuracy: 0.2418
- Accuracy Unlabeled: nan
- Accuracy Flat-road: 0.0351
- Accuracy Flat-sidewalk: 0.5938
- Accuracy Flat-crosswalk: 0.3236
- Accuracy Flat-cyclinglane: 0.0338
- Accuracy Flat-parkingdriveway: 0.0555
- Accuracy Flat-railtrack: nan
- Accuracy Flat-curb: 0.0006
- Accuracy Human-person: 0.0
- Accuracy Human-rider: 0.0003
- Accuracy Vehicle-car: 0.3388
- Accuracy Vehicle-truck: 0.0016
- Accuracy Vehicle-bus: 0.0
- Accuracy Vehicle-tramtrain: 0.2141
- Accuracy Vehicle-motorcycle: 0.0053
- Accuracy Vehicle-bicycle: 0.0
- Accuracy Vehicle-caravan: 0.0
- Accuracy Vehicle-cartrailer: 0.0888
- Accuracy Construction-building: 0.0391
- Accuracy Construction-door: 0.0
- Accuracy Construction-wall: 0.0074
- Accuracy Construction-fenceguardrail: 0.0239
- Accuracy Construction-bridge: 0.0
- Accuracy Construction-tunnel: nan
- Accuracy Construction-stairs: 0.0006
- Accuracy Object-pole: 0.0593
- Accuracy Object-trafficsign: 0.0
- Accuracy Object-trafficlight: 0.0665
- Accuracy Nature-vegetation: 0.0846
- Accuracy Nature-terrain: 0.0002
- Accuracy Sky: 0.0030
- Accuracy Void-ground: 0.0635
- Accuracy Void-dynamic: 0.0004
- Accuracy Void-static: 0.0720
- Accuracy Void-unclear: 0.0022
- Iou Unlabeled: 0.0
- Iou Flat-road: 0.0297
- Iou Flat-sidewalk: 0.4826
- Iou Flat-crosswalk: 0.0624
- Iou Flat-cyclinglane: 0.0279
- Iou Flat-parkingdriveway: 0.0203
- Iou Flat-railtrack: 0.0
- Iou Flat-curb: 0.0005
- Iou Human-person: 0.0
- Iou Human-rider: 0.0001
- Iou Vehicle-car: 0.1389
- Iou Vehicle-truck: 0.0000
- Iou Vehicle-bus: 0.0
- Iou Vehicle-tramtrain: 0.0013
- Iou Vehicle-motorcycle: 0.0007
- Iou Vehicle-bicycle: 0.0
- Iou Vehicle-caravan: 0.0
- Iou Vehicle-cartrailer: 0.0004
- Iou Construction-building: 0.0383
- Iou Construction-door: 0.0
- Iou Construction-wall: 0.0057
- Iou Construction-fenceguardrail: 0.0127
- Iou Construction-bridge: 0.0
- Iou Construction-tunnel: 0.0
- Iou Construction-stairs: 0.0001
- Iou Object-pole: 0.0085
- Iou Object-trafficsign: 0.0
- Iou Object-trafficlight: 0.0002
- Iou Nature-vegetation: 0.0818
- Iou Nature-terrain: 0.0002
- Iou Sky: 0.0027
- Iou Void-ground: 0.0115
- Iou Void-dynamic: 0.0001
- Iou Void-static: 0.0102
- Iou Void-unclear: 0.0021

## 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: 0.025

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Flat-road | Accuracy Flat-sidewalk | Accuracy Flat-crosswalk | Accuracy Flat-cyclinglane | Accuracy Flat-parkingdriveway | Accuracy Flat-railtrack | Accuracy Flat-curb | Accuracy Human-person | Accuracy Human-rider | Accuracy Vehicle-car | Accuracy Vehicle-truck | Accuracy Vehicle-bus | Accuracy Vehicle-tramtrain | Accuracy Vehicle-motorcycle | Accuracy Vehicle-bicycle | Accuracy Vehicle-caravan | Accuracy Vehicle-cartrailer | Accuracy Construction-building | Accuracy Construction-door | Accuracy Construction-wall | Accuracy Construction-fenceguardrail | Accuracy Construction-bridge | Accuracy Construction-tunnel | Accuracy Construction-stairs | Accuracy Object-pole | Accuracy Object-trafficsign | Accuracy Object-trafficlight | Accuracy Nature-vegetation | Accuracy Nature-terrain | Accuracy Sky | Accuracy Void-ground | Accuracy Void-dynamic | Accuracy Void-static | Accuracy Void-unclear | Iou Unlabeled | Iou Flat-road | Iou Flat-sidewalk | Iou Flat-crosswalk | Iou Flat-cyclinglane | Iou Flat-parkingdriveway | Iou Flat-railtrack | Iou Flat-curb | Iou Human-person | Iou Human-rider | Iou Vehicle-car | Iou Vehicle-truck | Iou Vehicle-bus | Iou Vehicle-tramtrain | Iou Vehicle-motorcycle | Iou Vehicle-bicycle | Iou Vehicle-caravan | Iou Vehicle-cartrailer | Iou Construction-building | Iou Construction-door | Iou Construction-wall | Iou Construction-fenceguardrail | Iou Construction-bridge | Iou Construction-tunnel | Iou Construction-stairs | Iou Object-pole | Iou Object-trafficsign | Iou Object-trafficlight | Iou Nature-vegetation | Iou Nature-terrain | Iou Sky | Iou Void-ground | Iou Void-dynamic | Iou Void-static | Iou Void-unclear |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:------------------:|:----------------------:|:-----------------------:|:-------------------------:|:-----------------------------:|:-----------------------:|:------------------:|:---------------------:|:--------------------:|:--------------------:|:----------------------:|:--------------------:|:--------------------------:|:---------------------------:|:------------------------:|:------------------------:|:---------------------------:|:------------------------------:|:--------------------------:|:--------------------------:|:------------------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:--------------------:|:---------------------------:|:----------------------------:|:--------------------------:|:-----------------------:|:------------:|:--------------------:|:---------------------:|:--------------------:|:---------------------:|:-------------:|:-------------:|:-----------------:|:------------------:|:--------------------:|:------------------------:|:------------------:|:-------------:|:----------------:|:---------------:|:---------------:|:-----------------:|:---------------:|:---------------------:|:----------------------:|:-------------------:|:-------------------:|:----------------------:|:-------------------------:|:---------------------:|:---------------------:|:-------------------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:---------------:|:----------------------:|:-----------------------:|:---------------------:|:------------------:|:-------:|:---------------:|:----------------:|:---------------:|:----------------:|
| 3.5028        | 0.01  | 5    | 3.5307          | 0.0194   | 0.0486        | 0.1779           | nan                | 0.0150             | 0.4721                 | 0.2351                  | 0.0249                    | 0.0409                        | nan                     | 0.0003             | 0.0                   | 0.0003               | 0.1461               | 0.0231                 | 0.0                  | 0.2163                     | 0.0047                      | 0.0                      | 0.0                      | 0.0318                      | 0.0223                         | 0.0003                     | 0.0136                     | 0.0166                               | 0.0                          | nan                          | 0.0008                       | 0.0511               | 0.0                         | 0.0665                       | 0.0261                     | 0.0005                  | 0.0010       | 0.0697               | 0.0014                | 0.0720               | 0.0020                | 0.0           | 0.0128        | 0.3979            | 0.0509             | 0.0221               | 0.0166                   | 0.0                | 0.0003        | 0.0              | 0.0001          | 0.0769          | 0.0000            | 0.0             | 0.0015                | 0.0003                 | 0.0                 | 0.0                 | 0.0001                 | 0.0219                    | 0.0001                | 0.0089                | 0.0103                          | 0.0                     | 0.0                     | 0.0001                  | 0.0070          | 0.0                    | 0.0001                  | 0.0257                | 0.0005             | 0.0009  | 0.0109          | 0.0004           | 0.0099          | 0.0019           |
| 3.3613        | 0.03  | 10   | 3.5116          | 0.0268   | 0.0661        | 0.2418           | nan                | 0.0351             | 0.5938                 | 0.3236                  | 0.0338                    | 0.0555                        | nan                     | 0.0006             | 0.0                   | 0.0003               | 0.3388               | 0.0016                 | 0.0                  | 0.2141                     | 0.0053                      | 0.0                      | 0.0                      | 0.0888                      | 0.0391                         | 0.0                        | 0.0074                     | 0.0239                               | 0.0                          | nan                          | 0.0006                       | 0.0593               | 0.0                         | 0.0665                       | 0.0846                     | 0.0002                  | 0.0030       | 0.0635               | 0.0004                | 0.0720               | 0.0022                | 0.0           | 0.0297        | 0.4826            | 0.0624             | 0.0279               | 0.0203                   | 0.0                | 0.0005        | 0.0              | 0.0001          | 0.1389          | 0.0000            | 0.0             | 0.0013                | 0.0007                 | 0.0                 | 0.0                 | 0.0004                 | 0.0383                    | 0.0                   | 0.0057                | 0.0127                          | 0.0                     | 0.0                     | 0.0001                  | 0.0085          | 0.0                    | 0.0002                  | 0.0818                | 0.0002             | 0.0027  | 0.0115          | 0.0001           | 0.0102          | 0.0021           |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "other", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "model-index": [{"name": "segformer-b0-finetuned-segments-sidewalk-2", "results": []}]}
transformers pytorch segformer vision image-segmentation generated_from_trainer license:other endpoints_compatible region:us
TAGS
#transformers #pytorch #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us 

segformer-b0-finetuned-segments-sidewalk-2
==========================================


This model is a fine-tuned version of nvidia/mit-b0 on the pixel\_values, the label and the {'pixel\_values': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1920x1080 at 0x7FCAFB662B60>, 'label': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=1x1 at 0x7FCAFB662B30>} datasets.
It achieves the following results on the evaluation set:


* Loss: 3.5116
* Mean Iou: 0.0268
* Mean Accuracy: 0.0661
* Overall Accuracy: 0.2418
* Accuracy Unlabeled: nan
* Accuracy Flat-road: 0.0351
* Accuracy Flat-sidewalk: 0.5938
* Accuracy Flat-crosswalk: 0.3236
* Accuracy Flat-cyclinglane: 0.0338
* Accuracy Flat-parkingdriveway: 0.0555
* Accuracy Flat-railtrack: nan
* Accuracy Flat-curb: 0.0006
* Accuracy Human-person: 0.0
* Accuracy Human-rider: 0.0003
* Accuracy Vehicle-car: 0.3388
* Accuracy Vehicle-truck: 0.0016
* Accuracy Vehicle-bus: 0.0
* Accuracy Vehicle-tramtrain: 0.2141
* Accuracy Vehicle-motorcycle: 0.0053
* Accuracy Vehicle-bicycle: 0.0
* Accuracy Vehicle-caravan: 0.0
* Accuracy Vehicle-cartrailer: 0.0888
* Accuracy Construction-building: 0.0391
* Accuracy Construction-door: 0.0
* Accuracy Construction-wall: 0.0074
* Accuracy Construction-fenceguardrail: 0.0239
* Accuracy Construction-bridge: 0.0
* Accuracy Construction-tunnel: nan
* Accuracy Construction-stairs: 0.0006
* Accuracy Object-pole: 0.0593
* Accuracy Object-trafficsign: 0.0
* Accuracy Object-trafficlight: 0.0665
* Accuracy Nature-vegetation: 0.0846
* Accuracy Nature-terrain: 0.0002
* Accuracy Sky: 0.0030
* Accuracy Void-ground: 0.0635
* Accuracy Void-dynamic: 0.0004
* Accuracy Void-static: 0.0720
* Accuracy Void-unclear: 0.0022
* Iou Unlabeled: 0.0
* Iou Flat-road: 0.0297
* Iou Flat-sidewalk: 0.4826
* Iou Flat-crosswalk: 0.0624
* Iou Flat-cyclinglane: 0.0279
* Iou Flat-parkingdriveway: 0.0203
* Iou Flat-railtrack: 0.0
* Iou Flat-curb: 0.0005
* Iou Human-person: 0.0
* Iou Human-rider: 0.0001
* Iou Vehicle-car: 0.1389
* Iou Vehicle-truck: 0.0000
* Iou Vehicle-bus: 0.0
* Iou Vehicle-tramtrain: 0.0013
* Iou Vehicle-motorcycle: 0.0007
* Iou Vehicle-bicycle: 0.0
* Iou Vehicle-caravan: 0.0
* Iou Vehicle-cartrailer: 0.0004
* Iou Construction-building: 0.0383
* Iou Construction-door: 0.0
* Iou Construction-wall: 0.0057
* Iou Construction-fenceguardrail: 0.0127
* Iou Construction-bridge: 0.0
* Iou Construction-tunnel: 0.0
* Iou Construction-stairs: 0.0001
* Iou Object-pole: 0.0085
* Iou Object-trafficsign: 0.0
* Iou Object-trafficlight: 0.0002
* Iou Nature-vegetation: 0.0818
* Iou Nature-terrain: 0.0002
* Iou Sky: 0.0027
* Iou Void-ground: 0.0115
* Iou Void-dynamic: 0.0001
* Iou Void-static: 0.0102
* Iou Void-unclear: 0.0021


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: 0.025


### Training results



### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* 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: 0.025 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #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: 0.025 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #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: 0.025### Training results### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3