transformers tensorboard safetensors segformer image-segmentation vision generated_from_trainer base_model:nvidia/mit-b0 license:other endpoints_compatible region:us
---
license: other
base_model: nvidia/mit-b0
tags:
- image-segmentation
- vision
- generated_from_trainer
model-index:
- name: segformer-finetuned-fingertip-10-steps
  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-finetuned-fingertip-10-steps

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: 3.5204
- Mean Iou: 0.0132
- Mean Accuracy: 0.0688
- Overall Accuracy: 0.0956
- Accuracy Unlabeled: nan
- Accuracy Flat-road: 0.0201
- Accuracy Flat-sidewalk: 0.1177
- Accuracy Flat-crosswalk: 0.0014
- Accuracy Flat-cyclinglane: 0.4426
- Accuracy Flat-parkingdriveway: 0.0021
- Accuracy Flat-railtrack: nan
- Accuracy Flat-curb: 0.0008
- Accuracy Human-person: 0.0010
- Accuracy Human-rider: 0.0077
- Accuracy Vehicle-car: 0.1566
- Accuracy Vehicle-truck: 0.0040
- Accuracy Vehicle-bus: 0.0
- Accuracy Vehicle-tramtrain: nan
- Accuracy Vehicle-motorcycle: 0.5317
- Accuracy Vehicle-bicycle: 0.0892
- Accuracy Vehicle-caravan: 0.0
- Accuracy Vehicle-cartrailer: nan
- Accuracy Construction-building: 0.0578
- Accuracy Construction-door: 0.0682
- Accuracy Construction-wall: 0.0002
- Accuracy Construction-fenceguardrail: 0.0000
- Accuracy Construction-bridge: 0.0
- Accuracy Construction-tunnel: nan
- Accuracy Construction-stairs: 0.0055
- Accuracy Object-pole: 0.0162
- Accuracy Object-trafficsign: 0.0
- Accuracy Object-trafficlight: 0.3811
- Accuracy Nature-vegetation: 0.0756
- Accuracy Nature-terrain: 0.0010
- Accuracy Sky: 0.0038
- Accuracy Void-ground: 0.0400
- Accuracy Void-dynamic: 0.0002
- Accuracy Void-static: 0.0386
- Accuracy Void-unclear: 0.0
- Iou Unlabeled: 0.0
- Iou Flat-road: 0.0193
- Iou Flat-sidewalk: 0.1141
- Iou Flat-crosswalk: 0.0013
- Iou Flat-cyclinglane: 0.0702
- Iou Flat-parkingdriveway: 0.0019
- Iou Flat-railtrack: 0.0
- Iou Flat-curb: 0.0007
- Iou Human-person: 0.0005
- Iou Human-rider: 0.0001
- Iou Vehicle-car: 0.1087
- Iou Vehicle-truck: 0.0003
- Iou Vehicle-bus: 0.0
- Iou Vehicle-tramtrain: 0.0
- Iou Vehicle-motorcycle: 0.0004
- Iou Vehicle-bicycle: 0.0085
- Iou Vehicle-caravan: 0.0
- Iou Vehicle-cartrailer: 0.0
- Iou Construction-building: 0.0413
- Iou Construction-door: 0.0067
- Iou Construction-wall: 0.0002
- Iou Construction-fenceguardrail: 0.0000
- Iou Construction-bridge: 0.0
- Iou Construction-tunnel: 0.0
- Iou Construction-stairs: 0.0021
- Iou Object-pole: 0.0036
- Iou Object-trafficsign: 0.0
- Iou Object-trafficlight: 0.0001
- Iou Nature-vegetation: 0.0663
- Iou Nature-terrain: 0.0009
- Iou Sky: 0.0038
- Iou Void-ground: 0.0049
- Iou Void-dynamic: 0.0000
- Iou Void-static: 0.0046
- 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: 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: 10

### 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:------------------:|:----------------------:|:-----------------------:|:-------------------------:|:-----------------------------:|:-----------------------:|:------------------:|:---------------------:|:--------------------:|:--------------------:|:----------------------:|:--------------------:|:--------------------------:|:---------------------------:|:------------------------:|:------------------------:|:---------------------------:|:------------------------------:|:--------------------------:|:--------------------------:|:------------------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:--------------------:|:---------------------------:|:----------------------------:|:--------------------------:|:-----------------------:|:------------:|:--------------------:|:---------------------:|:--------------------:|:---------------------:|:-------------:|:-------------:|:-----------------:|:------------------:|:--------------------:|:------------------------:|:------------------:|:-------------:|:----------------:|:---------------:|:---------------:|:-----------------:|:---------------:|:---------------------:|:----------------------:|:-------------------:|:-------------------:|:----------------------:|:-------------------------:|:---------------------:|:---------------------:|:-------------------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:---------------:|:----------------------:|:-----------------------:|:---------------------:|:------------------:|:-------:|:---------------:|:----------------:|:---------------:|:----------------:|
| No log        | 0.09  | 10   | 3.5204          | 0.0132   | 0.0688        | 0.0956           | nan                | 0.0201             | 0.1177                 | 0.0014                  | 0.4426                    | 0.0021                        | nan                     | 0.0008             | 0.0010                | 0.0077               | 0.1566               | 0.0040                 | 0.0                  | nan                        | 0.5317                      | 0.0892                   | 0.0                      | nan                         | 0.0578                         | 0.0682                     | 0.0002                     | 0.0000                               | 0.0                          | nan                          | 0.0055                       | 0.0162               | 0.0                         | 0.3811                       | 0.0756                     | 0.0010                  | 0.0038       | 0.0400               | 0.0002                | 0.0386               | 0.0                   | 0.0           | 0.0193        | 0.1141            | 0.0013             | 0.0702               | 0.0019                   | 0.0                | 0.0007        | 0.0005           | 0.0001          | 0.1087          | 0.0003            | 0.0             | 0.0                   | 0.0004                 | 0.0085              | 0.0                 | 0.0                    | 0.0413                    | 0.0067                | 0.0002                | 0.0000                          | 0.0                     | 0.0                     | 0.0021                  | 0.0036          | 0.0                    | 0.0001                  | 0.0663                | 0.0009             | 0.0038  | 0.0049          | 0.0000           | 0.0046          | 0.0              |


### Framework versions

- Transformers 4.37.0.dev0
- Pytorch 2.1.1+cu118
- Datasets 2.16.0
- Tokenizers 0.15.0
