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# segformer-b0-finetuned-segments-sidewalk

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5679
- Miou: 0.2769
- Macc: 0.3331
- Overall Accuracy: 0.8424
- Per Category Iou: [nan, 0.7174911859423314, 0.8790751054409742, 0.6065232798410057, 0.6975274018055722, 0.3486407385349508, nan, 0.40093167116703843, 0.28779837903852556, 0.0, 0.7870339041746186, 0.0, 0.0, 0.0, 0.0, 0.1464360606454247, 0.0, 0.0, 0.6770283275082656, 0.0, 0.338555175257431, 0.14697310016578427, 0.0, nan, 0.0, 0.27163002251763635, 0.0, 0.0, 0.8257437911843676, 0.7169333376341568, 0.9108105550493353, 0.0, 0.0, 0.1016801552778885, 0.0]
- Per Category Accuracy: [nan, 0.9199960254104915, 0.9327745517652714, 0.7304629327758765, 0.7378309547498484, 0.45295941407150275, nan, 0.5188608021128075, 0.5327441812670195, 0.0, 0.9353764765979435, 0.0, 0.0, 0.0, 0.0, 0.1588525415198792, 0.0, 0.0, 0.9238854794385364, 0.0, 0.4400394213522207, 0.15130051149615126, 0.0, nan, 0.0, 0.3570096986572905, 0.0, 0.0, 0.9359897980968498, 0.8570458108260572, 0.9549583230619891, 0.0, 0.0, 0.11786971668879294, 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: 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Miou   | Macc   | Overall Accuracy | Per Category Iou                                                                                                                                                                                                                                                                                                                                                                                                                                    | Per Category Accuracy                                                                                                                                                                                                                                                                                                                                                                                                                                  |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:----------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 1.357         | 1.0   | 400  | 1.0006          | 0.1632 | 0.2069 | 0.7524           | [nan, 0.5642795884663824, 0.7491853309192827, 0.0, 0.40589649630192104, 0.02723606910696284, nan, 0.0002207740938439576, 0.0, 0.0, 0.6632462867093903, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5671699281129761, 0.0, 0.0009207911027492868, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.7507253434892517, 0.6157793573905029, 0.8774768871968204, 0.0, 0.0, 0.0, 0.0]                                                                                        | [nan, 0.6839993330882016, 0.9786792586618772, 0.0, 0.4818162160949784, 0.02785198456498826, nan, 0.00022133459131411787, 0.0, 0.0, 0.9043689536433023, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8606078323791991, 0.0, 0.0009210330367246509, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.895198618615298, 0.8549807032886052, 0.9328734839751688, 0.0, 0.0, 0.0, 0.0]                                                                                            |
| 1.6346        | 2.0   | 800  | 0.7856          | 0.1903 | 0.2334 | 0.7917           | [nan, 0.6276046255936906, 0.8379492348238635, 0.0, 0.5220035981992285, 0.19441920935217594, nan, 0.16135703555333, 0.0, 0.0, 0.7357165628674137, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.567598980063164, 0.0, 0.07867871139133086, 0.0, 0.0, nan, 0.0, 0.02123705398363847, 0.0, 0.0, 0.7917172051343153, 0.6589515948064048, 0.8916684207946344, 0.0, 0.0, 0.00013685918191589503, 0.0]                                                              | [nan, 0.8610263337355926, 0.9499345560017969, 0.0, 0.5908796687797819, 0.2144081438468206, nan, 0.1813236746419022, 0.0, 0.0, 0.8825551027577866, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.9239907140298015, 0.0, 0.08495225520298297, 0.0, 0.0, nan, 0.0, 0.021302829364985724, 0.0, 0.0, 0.9258397010509258, 0.8834861376443207, 0.9489131468773239, 0.0, 0.0, 0.0001372777815910495, 0.0]                                                               |
| 0.659         | 3.0   | 1200 | 0.6798          | 0.2215 | 0.2687 | 0.8107           | [nan, 0.6728474586764454, 0.8404607924530816, 0.21147709475332813, 0.5407350347311378, 0.23535489130104167, nan, 0.3087159264982809, 0.0060319580742948155, 0.0, 0.7331305064022374, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.6378031991744924, 0.0, 0.35289337122777764, 6.24997656258789e-05, 0.0, nan, 0.0, 0.14698390926256938, 0.0, 0.0, 0.8019042204623998, 0.669283249725758, 0.8928145424856038, 0.0, 0.0, 0.03847722460691187, 0.0]            | [nan, 0.866012011452706, 0.9627112260298595, 0.21236715482371135, 0.5645869262075475, 0.2750610095322395, nan, 0.3857655597748765, 0.0060319580742948155, 0.0, 0.939196440844118, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8380282443529743, 0.0, 0.5749902063170915, 6.256068386334744e-05, 0.0, nan, 0.0, 0.1605725590139305, 0.0, 0.0, 0.9212803460870584, 0.8870298583701837, 0.959700359744241, 0.0, 0.0, 0.04453994364914478, 0.0]                   |
| 0.5481        | 4.0   | 1600 | 0.5999          | 0.2522 | 0.2998 | 0.8312           | [nan, 0.7078353465279917, 0.8661728761172196, 0.3857324719136883, 0.6338278880825696, 0.3440050078187208, nan, 0.35980405625532347, 0.23875867241702606, 0.0, 0.773703347865372, 0.0, 0.0, 0.0, 0.0, 0.0004931363471679884, 0.0, 0.0, 0.6554146448850521, 0.0, 0.367673493717809, 0.03089804641909161, 0.0, nan, 0.0, 0.21529017459808872, 0.0, 0.0, 0.818951849158376, 0.7007504838794707, 0.9053929635423027, 0.0, 0.0, 0.06626212301200333, 0.0] | [nan, 0.8955207784307155, 0.9536263694097721, 0.39712577675621036, 0.6989299616008556, 0.4248959179453637, nan, 0.42984959564233455, 0.26168627652468784, 0.0, 0.9055166364779607, 0.0, 0.0, 0.0, 0.0, 0.0004932058379466533, 0.0, 0.0, 0.8632164276000204, 0.0, 0.6365580872107307, 0.031401709658368616, 0.0, nan, 0.0, 0.2497286263775161, 0.0, 0.0, 0.9296676429517725, 0.8858954297713482, 0.9555756265860916, 0.0, 0.0, 0.0750792276952902, 0.0] |
| 0.7855        | 5.0   | 2000 | 0.5679          | 0.2769 | 0.3331 | 0.8424           | [nan, 0.7174911859423314, 0.8790751054409742, 0.6065232798410057, 0.6975274018055722, 0.3486407385349508, nan, 0.40093167116703843, 0.28779837903852556, 0.0, 0.7870339041746186, 0.0, 0.0, 0.0, 0.0, 0.1464360606454247, 0.0, 0.0, 0.6770283275082656, 0.0, 0.338555175257431, 0.14697310016578427, 0.0, nan, 0.0, 0.27163002251763635, 0.0, 0.0, 0.8257437911843676, 0.7169333376341568, 0.9108105550493353, 0.0, 0.0, 0.1016801552778885, 0.0]   | [nan, 0.9199960254104915, 0.9327745517652714, 0.7304629327758765, 0.7378309547498484, 0.45295941407150275, nan, 0.5188608021128075, 0.5327441812670195, 0.0, 0.9353764765979435, 0.0, 0.0, 0.0, 0.0, 0.1588525415198792, 0.0, 0.0, 0.9238854794385364, 0.0, 0.4400394213522207, 0.15130051149615126, 0.0, nan, 0.0, 0.3570096986572905, 0.0, 0.0, 0.9359897980968498, 0.8570458108260572, 0.9549583230619891, 0.0, 0.0, 0.11786971668879294, 0.0]      |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6

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

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


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


* Loss: 0.5679
* Miou: 0.2769
* Macc: 0.3331
* Overall Accuracy: 0.8424
* Per Category Iou: [nan, 0.7174911859423314, 0.8790751054409742, 0.6065232798410057, 0.6975274018055722, 0.3486407385349508, nan, 0.40093167116703843, 0.28779837903852556, 0.0, 0.7870339041746186, 0.0, 0.0, 0.0, 0.0, 0.1464360606454247, 0.0, 0.0, 0.6770283275082656, 0.0, 0.338555175257431, 0.14697310016578427, 0.0, nan, 0.0, 0.27163002251763635, 0.0, 0.0, 0.8257437911843676, 0.7169333376341568, 0.9108105550493353, 0.0, 0.0, 0.1016801552778885, 0.0]
* Per Category Accuracy: [nan, 0.9199960254104915, 0.9327745517652714, 0.7304629327758765, 0.7378309547498484, 0.45295941407150275, nan, 0.5188608021128075, 0.5327441812670195, 0.0, 0.9353764765979435, 0.0, 0.0, 0.0, 0.0, 0.1588525415198792, 0.0, 0.0, 0.9238854794385364, 0.0, 0.4400394213522207, 0.15130051149615126, 0.0, nan, 0.0, 0.3570096986572905, 0.0, 0.0, 0.9359897980968498, 0.8570458108260572, 0.9549583230619891, 0.0, 0.0, 0.11786971668879294, 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: 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: 5


### Training results



### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.0+cu111
* Datasets 1.18.3
* Tokenizers 0.11.6



### 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: 5 ### Training results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.0+cu111
* Datasets 1.18.3
* Tokenizers 0.11.6
TAGS
#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-apache-2.0 #endpoints_compatible #has_space #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: 5 ### Training results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.0+cu111
* Datasets 1.18.3
* Tokenizers 0.11.6
passage: TAGS
#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-apache-2.0 #endpoints_compatible #has_space #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: 5### Training results### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.0+cu111
* Datasets 1.18.3
* Tokenizers 0.11.6