---
license: other
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b0-finetuned-segments-sidewalk-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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 |
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| 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