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# segformer-trainer-test-bis

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: 1.3784
- Mean Iou: 0.1424
- Mean Accuracy: 0.1896
- Overall Accuracy: 0.7288
- Accuracy Unlabeled: nan
- Accuracy Flat-road: 0.6651
- Accuracy Flat-sidewalk: 0.9129
- Accuracy Flat-crosswalk: 0.0
- Accuracy Flat-cyclinglane: 0.5829
- Accuracy Flat-parkingdriveway: 0.0184
- Accuracy Flat-railtrack: 0.0
- Accuracy Flat-curb: 0.0
- Accuracy Human-person: 0.0
- Accuracy Human-rider: 0.0
- Accuracy Vehicle-car: 0.8322
- Accuracy Vehicle-truck: 0.0
- Accuracy Vehicle-bus: 0.0
- Accuracy Vehicle-tramtrain: 0.0
- Accuracy Vehicle-motorcycle: 0.0
- Accuracy Vehicle-bicycle: 0.0
- Accuracy Vehicle-caravan: 0.0
- Accuracy Vehicle-cartrailer: 0.0
- Accuracy Construction-building: 0.8930
- Accuracy Construction-door: 0.0
- Accuracy Construction-wall: 0.0025
- Accuracy Construction-fenceguardrail: 0.0
- Accuracy Construction-bridge: 0.0
- Accuracy Construction-tunnel: 0.0
- Accuracy Construction-stairs: 0.0
- Accuracy Object-pole: 0.0008
- Accuracy Object-trafficsign: 0.0
- Accuracy Object-trafficlight: 0.0
- Accuracy Nature-vegetation: 0.8552
- Accuracy Nature-terrain: 0.8507
- Accuracy Sky: 0.8336
- Accuracy Void-ground: 0.0
- Accuracy Void-dynamic: 0.0
- Accuracy Void-static: 0.0
- Accuracy Void-unclear: 0.0
- Iou Unlabeled: nan
- Iou Flat-road: 0.4712
- Iou Flat-sidewalk: 0.7651
- Iou Flat-crosswalk: 0.0
- Iou Flat-cyclinglane: 0.5216
- Iou Flat-parkingdriveway: 0.0178
- Iou Flat-railtrack: 0.0
- Iou Flat-curb: 0.0
- Iou Human-person: 0.0
- Iou Human-rider: 0.0
- Iou Vehicle-car: 0.5696
- Iou Vehicle-truck: 0.0
- Iou Vehicle-bus: 0.0
- Iou Vehicle-tramtrain: 0.0
- Iou Vehicle-motorcycle: 0.0
- Iou Vehicle-bicycle: 0.0
- Iou Vehicle-caravan: 0.0
- Iou Vehicle-cartrailer: 0.0
- Iou Construction-building: 0.4716
- Iou Construction-door: 0.0
- Iou Construction-wall: 0.0024
- Iou Construction-fenceguardrail: 0.0
- Iou Construction-bridge: 0.0
- Iou Construction-tunnel: 0.0
- Iou Construction-stairs: 0.0
- Iou Object-pole: 0.0008
- Iou Object-trafficsign: 0.0
- Iou Object-trafficlight: 0.0
- Iou Nature-vegetation: 0.6813
- Iou Nature-terrain: 0.5513
- Iou Sky: 0.7873
- Iou Void-ground: 0.0
- Iou Void-dynamic: 0.0
- Iou Void-static: 0.0
- Iou Void-unclear: 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0

### Training results



### Framework versions

- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6

{"license": "apache-2.0", "tags": ["image-segmentation", "vision", "generated_from_trainer"], "base_model": "nvidia/mit-b0", "model-index": [{"name": "segformer-trainer-test-bis", "results": []}]}
transformers pytorch tensorboard segformer image-segmentation vision generated_from_trainer base_model:nvidia/mit-b0 license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #segformer #image-segmentation #vision #generated_from_trainer #base_model-nvidia/mit-b0 #license-apache-2.0 #endpoints_compatible #region-us 




# segformer-trainer-test-bis

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: 1.3784
- Mean Iou: 0.1424
- Mean Accuracy: 0.1896
- Overall Accuracy: 0.7288
- Accuracy Unlabeled: nan
- Accuracy Flat-road: 0.6651
- Accuracy Flat-sidewalk: 0.9129
- Accuracy Flat-crosswalk: 0.0
- Accuracy Flat-cyclinglane: 0.5829
- Accuracy Flat-parkingdriveway: 0.0184
- Accuracy Flat-railtrack: 0.0
- Accuracy Flat-curb: 0.0
- Accuracy Human-person: 0.0
- Accuracy Human-rider: 0.0
- Accuracy Vehicle-car: 0.8322
- Accuracy Vehicle-truck: 0.0
- Accuracy Vehicle-bus: 0.0
- Accuracy Vehicle-tramtrain: 0.0
- Accuracy Vehicle-motorcycle: 0.0
- Accuracy Vehicle-bicycle: 0.0
- Accuracy Vehicle-caravan: 0.0
- Accuracy Vehicle-cartrailer: 0.0
- Accuracy Construction-building: 0.8930
- Accuracy Construction-door: 0.0
- Accuracy Construction-wall: 0.0025
- Accuracy Construction-fenceguardrail: 0.0
- Accuracy Construction-bridge: 0.0
- Accuracy Construction-tunnel: 0.0
- Accuracy Construction-stairs: 0.0
- Accuracy Object-pole: 0.0008
- Accuracy Object-trafficsign: 0.0
- Accuracy Object-trafficlight: 0.0
- Accuracy Nature-vegetation: 0.8552
- Accuracy Nature-terrain: 0.8507
- Accuracy Sky: 0.8336
- Accuracy Void-ground: 0.0
- Accuracy Void-dynamic: 0.0
- Accuracy Void-static: 0.0
- Accuracy Void-unclear: 0.0
- Iou Unlabeled: nan
- Iou Flat-road: 0.4712
- Iou Flat-sidewalk: 0.7651
- Iou Flat-crosswalk: 0.0
- Iou Flat-cyclinglane: 0.5216
- Iou Flat-parkingdriveway: 0.0178
- Iou Flat-railtrack: 0.0
- Iou Flat-curb: 0.0
- Iou Human-person: 0.0
- Iou Human-rider: 0.0
- Iou Vehicle-car: 0.5696
- Iou Vehicle-truck: 0.0
- Iou Vehicle-bus: 0.0
- Iou Vehicle-tramtrain: 0.0
- Iou Vehicle-motorcycle: 0.0
- Iou Vehicle-bicycle: 0.0
- Iou Vehicle-caravan: 0.0
- Iou Vehicle-cartrailer: 0.0
- Iou Construction-building: 0.4716
- Iou Construction-door: 0.0
- Iou Construction-wall: 0.0024
- Iou Construction-fenceguardrail: 0.0
- Iou Construction-bridge: 0.0
- Iou Construction-tunnel: 0.0
- Iou Construction-stairs: 0.0
- Iou Object-pole: 0.0008
- Iou Object-trafficsign: 0.0
- Iou Object-trafficlight: 0.0
- Iou Nature-vegetation: 0.6813
- Iou Nature-terrain: 0.5513
- Iou Sky: 0.7873
- Iou Void-ground: 0.0
- Iou Void-dynamic: 0.0
- Iou Void-static: 0.0
- Iou Void-unclear: 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0

### Training results



### Framework versions

- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6

# segformer-trainer-test-bis

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: 1.3784
- Mean Iou: 0.1424
- Mean Accuracy: 0.1896
- Overall Accuracy: 0.7288
- Accuracy Unlabeled: nan
- Accuracy Flat-road: 0.6651
- Accuracy Flat-sidewalk: 0.9129
- Accuracy Flat-crosswalk: 0.0
- Accuracy Flat-cyclinglane: 0.5829
- Accuracy Flat-parkingdriveway: 0.0184
- Accuracy Flat-railtrack: 0.0
- Accuracy Flat-curb: 0.0
- Accuracy Human-person: 0.0
- Accuracy Human-rider: 0.0
- Accuracy Vehicle-car: 0.8322
- Accuracy Vehicle-truck: 0.0
- Accuracy Vehicle-bus: 0.0
- Accuracy Vehicle-tramtrain: 0.0
- Accuracy Vehicle-motorcycle: 0.0
- Accuracy Vehicle-bicycle: 0.0
- Accuracy Vehicle-caravan: 0.0
- Accuracy Vehicle-cartrailer: 0.0
- Accuracy Construction-building: 0.8930
- Accuracy Construction-door: 0.0
- Accuracy Construction-wall: 0.0025
- Accuracy Construction-fenceguardrail: 0.0
- Accuracy Construction-bridge: 0.0
- Accuracy Construction-tunnel: 0.0
- Accuracy Construction-stairs: 0.0
- Accuracy Object-pole: 0.0008
- Accuracy Object-trafficsign: 0.0
- Accuracy Object-trafficlight: 0.0
- Accuracy Nature-vegetation: 0.8552
- Accuracy Nature-terrain: 0.8507
- Accuracy Sky: 0.8336
- Accuracy Void-ground: 0.0
- Accuracy Void-dynamic: 0.0
- Accuracy Void-static: 0.0
- Accuracy Void-unclear: 0.0
- Iou Unlabeled: nan
- Iou Flat-road: 0.4712
- Iou Flat-sidewalk: 0.7651
- Iou Flat-crosswalk: 0.0
- Iou Flat-cyclinglane: 0.5216
- Iou Flat-parkingdriveway: 0.0178
- Iou Flat-railtrack: 0.0
- Iou Flat-curb: 0.0
- Iou Human-person: 0.0
- Iou Human-rider: 0.0
- Iou Vehicle-car: 0.5696
- Iou Vehicle-truck: 0.0
- Iou Vehicle-bus: 0.0
- Iou Vehicle-tramtrain: 0.0
- Iou Vehicle-motorcycle: 0.0
- Iou Vehicle-bicycle: 0.0
- Iou Vehicle-caravan: 0.0
- Iou Vehicle-cartrailer: 0.0
- Iou Construction-building: 0.4716
- Iou Construction-door: 0.0
- Iou Construction-wall: 0.0024
- Iou Construction-fenceguardrail: 0.0
- Iou Construction-bridge: 0.0
- Iou Construction-tunnel: 0.0
- Iou Construction-stairs: 0.0
- Iou Object-pole: 0.0008
- Iou Object-trafficsign: 0.0
- Iou Object-trafficlight: 0.0
- Iou Nature-vegetation: 0.6813
- Iou Nature-terrain: 0.5513
- Iou Sky: 0.7873
- Iou Void-ground: 0.0
- Iou Void-dynamic: 0.0
- Iou Void-static: 0.0
- Iou Void-unclear: 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0 ### Training results ### Framework versions

- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
TAGS
#transformers #pytorch #tensorboard #segformer #image-segmentation #vision #generated_from_trainer #base_model-nvidia/mit-b0 #license-apache-2.0 #endpoints_compatible #region-us 
 # segformer-trainer-test-bis

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: 1.3784
- Mean Iou: 0.1424
- Mean Accuracy: 0.1896
- Overall Accuracy: 0.7288
- Accuracy Unlabeled: nan
- Accuracy Flat-road: 0.6651
- Accuracy Flat-sidewalk: 0.9129
- Accuracy Flat-crosswalk: 0.0
- Accuracy Flat-cyclinglane: 0.5829
- Accuracy Flat-parkingdriveway: 0.0184
- Accuracy Flat-railtrack: 0.0
- Accuracy Flat-curb: 0.0
- Accuracy Human-person: 0.0
- Accuracy Human-rider: 0.0
- Accuracy Vehicle-car: 0.8322
- Accuracy Vehicle-truck: 0.0
- Accuracy Vehicle-bus: 0.0
- Accuracy Vehicle-tramtrain: 0.0
- Accuracy Vehicle-motorcycle: 0.0
- Accuracy Vehicle-bicycle: 0.0
- Accuracy Vehicle-caravan: 0.0
- Accuracy Vehicle-cartrailer: 0.0
- Accuracy Construction-building: 0.8930
- Accuracy Construction-door: 0.0
- Accuracy Construction-wall: 0.0025
- Accuracy Construction-fenceguardrail: 0.0
- Accuracy Construction-bridge: 0.0
- Accuracy Construction-tunnel: 0.0
- Accuracy Construction-stairs: 0.0
- Accuracy Object-pole: 0.0008
- Accuracy Object-trafficsign: 0.0
- Accuracy Object-trafficlight: 0.0
- Accuracy Nature-vegetation: 0.8552
- Accuracy Nature-terrain: 0.8507
- Accuracy Sky: 0.8336
- Accuracy Void-ground: 0.0
- Accuracy Void-dynamic: 0.0
- Accuracy Void-static: 0.0
- Accuracy Void-unclear: 0.0
- Iou Unlabeled: nan
- Iou Flat-road: 0.4712
- Iou Flat-sidewalk: 0.7651
- Iou Flat-crosswalk: 0.0
- Iou Flat-cyclinglane: 0.5216
- Iou Flat-parkingdriveway: 0.0178
- Iou Flat-railtrack: 0.0
- Iou Flat-curb: 0.0
- Iou Human-person: 0.0
- Iou Human-rider: 0.0
- Iou Vehicle-car: 0.5696
- Iou Vehicle-truck: 0.0
- Iou Vehicle-bus: 0.0
- Iou Vehicle-tramtrain: 0.0
- Iou Vehicle-motorcycle: 0.0
- Iou Vehicle-bicycle: 0.0
- Iou Vehicle-caravan: 0.0
- Iou Vehicle-cartrailer: 0.0
- Iou Construction-building: 0.4716
- Iou Construction-door: 0.0
- Iou Construction-wall: 0.0024
- Iou Construction-fenceguardrail: 0.0
- Iou Construction-bridge: 0.0
- Iou Construction-tunnel: 0.0
- Iou Construction-stairs: 0.0
- Iou Object-pole: 0.0008
- Iou Object-trafficsign: 0.0
- Iou Object-trafficlight: 0.0
- Iou Nature-vegetation: 0.6813
- Iou Nature-terrain: 0.5513
- Iou Sky: 0.7873
- Iou Void-ground: 0.0
- Iou Void-dynamic: 0.0
- Iou Void-static: 0.0
- Iou Void-unclear: 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0 ### Training results ### Framework versions

- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
passage: TAGS
#transformers #pytorch #tensorboard #segformer #image-segmentation #vision #generated_from_trainer #base_model-nvidia/mit-b0 #license-apache-2.0 #endpoints_compatible #region-us 
