transformers pytorch segformer vision image-segmentation generated_from_trainer base_model:nvidia/mit-b1 license:other endpoints_compatible region:us
---
license: other
base_model: nvidia/mit-b1
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b1-finetuned-Hiking
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# segformer-b1-finetuned-Hiking

This model is a fine-tuned version of [nvidia/mit-b1](https://huggingface.co/nvidia/mit-b1) on the twdent/Hiking dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1129
- Mean Iou: 0.6261
- Mean Accuracy: 0.9707
- Overall Accuracy: 0.9700
- Accuracy Unlabeled: nan
- Accuracy Traversable: 0.9730
- Accuracy Non-traversable: 0.9684
- Iou Unlabeled: 0.0
- Iou Traversable: 0.9226
- Iou Non-traversable: 0.9557

## 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: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Traversable | Accuracy Non-traversable | Iou Unlabeled | Iou Traversable | Iou Non-traversable |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:--------------------:|:------------------------:|:-------------:|:---------------:|:-------------------:|
| 0.5526        | 1.33  | 20   | 0.7232          | 0.5814   | 0.9404        | 0.9333           | nan                | 0.9630               | 0.9177                   | 0.0           | 0.8433          | 0.9009              |
| 0.3768        | 2.67  | 40   | 0.3616          | 0.5929   | 0.9504        | 0.9453           | nan                | 0.9666               | 0.9341                   | 0.0           | 0.8605          | 0.9183              |
| 0.3271        | 4.0   | 60   | 0.2683          | 0.6080   | 0.9606        | 0.9571           | nan                | 0.9718               | 0.9494                   | 0.0           | 0.8883          | 0.9358              |
| 0.2377        | 5.33  | 80   | 0.2754          | 0.5869   | 0.9256        | 0.9429           | nan                | 0.8706               | 0.9807                   | 0.0           | 0.8415          | 0.9191              |
| 0.2242        | 6.67  | 100  | 0.2299          | 0.6027   | 0.9447        | 0.9545           | nan                | 0.9134               | 0.9760                   | 0.0           | 0.8739          | 0.9341              |
| 0.2458        | 8.0   | 120  | 0.1939          | 0.6207   | 0.9604        | 0.9667           | nan                | 0.9402               | 0.9805                   | 0.0           | 0.9107          | 0.9513              |
| 0.1541        | 9.33  | 140  | 0.1988          | 0.6121   | 0.9654        | 0.9599           | nan                | 0.9831               | 0.9477                   | 0.0           | 0.8967          | 0.9395              |
| 0.1448        | 10.67 | 160  | 0.1722          | 0.6202   | 0.9677        | 0.9662           | nan                | 0.9725               | 0.9629                   | 0.0           | 0.9112          | 0.9494              |
| 0.1533        | 12.0  | 180  | 0.2112          | 0.5951   | 0.9570        | 0.9454           | nan                | 0.9941               | 0.9199                   | 0.0           | 0.8676          | 0.9176              |
| 0.107         | 13.33 | 200  | 0.1658          | 0.6139   | 0.9626        | 0.9614           | nan                | 0.9665               | 0.9587                   | 0.0           | 0.8992          | 0.9426              |
| 0.109         | 14.67 | 220  | 0.1342          | 0.6267   | 0.9714        | 0.9712           | nan                | 0.9724               | 0.9705                   | 0.0           | 0.9231          | 0.9569              |
| 0.1092        | 16.0  | 240  | 0.1448          | 0.6173   | 0.9690        | 0.9636           | nan                | 0.9860               | 0.9519                   | 0.0           | 0.9065          | 0.9453              |
| 0.0971        | 17.33 | 260  | 0.1282          | 0.6216   | 0.9691        | 0.9673           | nan                | 0.9747               | 0.9635                   | 0.0           | 0.9136          | 0.9512              |
| 0.1448        | 18.67 | 280  | 0.1504          | 0.6155   | 0.9661        | 0.9619           | nan                | 0.9795               | 0.9526                   | 0.0           | 0.9032          | 0.9434              |
| 0.0797        | 20.0  | 300  | 0.1312          | 0.6209   | 0.9669        | 0.9666           | nan                | 0.9680               | 0.9659                   | 0.0           | 0.9124          | 0.9503              |
| 0.0766        | 21.33 | 320  | 0.1164          | 0.6251   | 0.9667        | 0.9696           | nan                | 0.9574               | 0.9760                   | 0.0           | 0.9198          | 0.9555              |
| 0.0822        | 22.67 | 340  | 0.1365          | 0.6171   | 0.9638        | 0.9639           | nan                | 0.9635               | 0.9641                   | 0.0           | 0.9050          | 0.9464              |
| 0.075         | 24.0  | 360  | 0.1401          | 0.6160   | 0.9679        | 0.9621           | nan                | 0.9862               | 0.9495                   | 0.0           | 0.9046          | 0.9433              |
| 0.0684        | 25.33 | 380  | 0.1317          | 0.6190   | 0.9687        | 0.9645           | nan                | 0.9822               | 0.9552                   | 0.0           | 0.9099          | 0.9472              |
| 0.0767        | 26.67 | 400  | 0.1293          | 0.6195   | 0.9699        | 0.9651           | nan                | 0.9851               | 0.9547                   | 0.0           | 0.9107          | 0.9478              |
| 0.0576        | 28.0  | 420  | 0.1195          | 0.6236   | 0.9701        | 0.9679           | nan                | 0.9771               | 0.9631                   | 0.0           | 0.9180          | 0.9529              |
| 0.0596        | 29.33 | 440  | 0.1179          | 0.6248   | 0.9717        | 0.9692           | nan                | 0.9794               | 0.9639                   | 0.0           | 0.9204          | 0.9541              |
| 0.0564        | 30.67 | 460  | 0.1110          | 0.6264   | 0.9719        | 0.9701           | nan                | 0.9777               | 0.9661                   | 0.0           | 0.9233          | 0.9559              |
| 0.0496        | 32.0  | 480  | 0.1063          | 0.6284   | 0.9726        | 0.9714           | nan                | 0.9761               | 0.9690                   | 0.0           | 0.9271          | 0.9581              |
| 0.0722        | 33.33 | 500  | 0.1073          | 0.6272   | 0.9712        | 0.9711           | nan                | 0.9716               | 0.9708                   | 0.0           | 0.9244          | 0.9573              |
| 0.0465        | 34.67 | 520  | 0.1228          | 0.6220   | 0.9692        | 0.9669           | nan                | 0.9763               | 0.9620                   | 0.0           | 0.9150          | 0.9510              |
| 0.0655        | 36.0  | 540  | 0.1142          | 0.6245   | 0.9704        | 0.9689           | nan                | 0.9752               | 0.9656                   | 0.0           | 0.9196          | 0.9540              |
| 0.0516        | 37.33 | 560  | 0.1197          | 0.6238   | 0.9687        | 0.9684           | nan                | 0.9696               | 0.9677                   | 0.0           | 0.9181          | 0.9533              |
| 0.0774        | 38.67 | 580  | 0.1114          | 0.6266   | 0.9706        | 0.9704           | nan                | 0.9712               | 0.9700                   | 0.0           | 0.9234          | 0.9565              |
| 0.0572        | 40.0  | 600  | 0.1124          | 0.6261   | 0.9707        | 0.9700           | nan                | 0.9730               | 0.9684                   | 0.0           | 0.9227          | 0.9558              |
| 0.0554        | 41.33 | 620  | 0.1116          | 0.6273   | 0.9718        | 0.9709           | nan                | 0.9747               | 0.9688                   | 0.0           | 0.9248          | 0.9570              |
| 0.0438        | 42.67 | 640  | 0.1192          | 0.6259   | 0.9707        | 0.9694           | nan                | 0.9746               | 0.9667                   | 0.0           | 0.9225          | 0.9551              |
| 0.0486        | 44.0  | 660  | 0.1186          | 0.6248   | 0.9709        | 0.9689           | nan                | 0.9775               | 0.9643                   | 0.0           | 0.9203          | 0.9540              |
| 0.0582        | 45.33 | 680  | 0.1194          | 0.6250   | 0.9705        | 0.9691           | nan                | 0.9751               | 0.9660                   | 0.0           | 0.9209          | 0.9542              |
| 0.0643        | 46.67 | 700  | 0.1157          | 0.6252   | 0.9706        | 0.9692           | nan                | 0.9750               | 0.9662                   | 0.0           | 0.9209          | 0.9546              |
| 0.057         | 48.0  | 720  | 0.1181          | 0.6251   | 0.9708        | 0.9691           | nan                | 0.9763               | 0.9654                   | 0.0           | 0.9210          | 0.9543              |
| 0.0468        | 49.33 | 740  | 0.1129          | 0.6261   | 0.9707        | 0.9700           | nan                | 0.9730               | 0.9684                   | 0.0           | 0.9226          | 0.9557              |


### Framework versions

- Transformers 4.35.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0
