---
license: other
tags:
- vision
- image-segmentation
- generated_from_trainer
base_model: nvidia/mit-b0
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 eleninaneversmiles/wheels dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1170

## 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: 300

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.9481        | 4.0   | 20   | 3.5435          |
| 2.6731        | 8.0   | 40   | 3.0067          |
| 2.6124        | 12.0  | 60   | 2.4952          |
| 2.0959        | 16.0  | 80   | 2.0672          |
| 2.0621        | 20.0  | 100  | 1.8896          |
| 1.8677        | 24.0  | 120  | 1.6907          |
| 1.7867        | 28.0  | 140  | 1.5101          |
| 1.3991        | 32.0  | 160  | 1.3829          |
| 1.1594        | 36.0  | 180  | 1.2332          |
| 1.0339        | 40.0  | 200  | 1.0889          |
| 0.8877        | 44.0  | 220  | 0.9699          |
| 0.9484        | 48.0  | 240  | 0.8998          |
| 0.8533        | 52.0  | 260  | 0.7505          |
| 0.5803        | 56.0  | 280  | 0.6548          |
| 0.5161        | 60.0  | 300  | 0.5719          |
| 0.4307        | 64.0  | 320  | 0.5128          |
| 0.3615        | 68.0  | 340  | 0.4228          |
| 0.3185        | 72.0  | 360  | 0.3733          |
| 0.2852        | 76.0  | 380  | 0.3421          |
| 0.299         | 80.0  | 400  | 0.3033          |
| 0.2014        | 84.0  | 420  | 0.2814          |
| 0.2139        | 88.0  | 440  | 0.2346          |
| 0.147         | 92.0  | 460  | 0.2176          |
| 0.1386        | 96.0  | 480  | 0.2030          |
| 0.1189        | 100.0 | 500  | 0.1950          |
| 0.1527        | 104.0 | 520  | 0.1804          |
| 0.093         | 108.0 | 540  | 0.1708          |
| 0.0934        | 112.0 | 560  | 0.1712          |
| 0.0878        | 116.0 | 580  | 0.1582          |
| 0.1096        | 120.0 | 600  | 0.1553          |
| 0.0743        | 124.0 | 620  | 0.1460          |
| 0.0677        | 128.0 | 640  | 0.1413          |
| 0.0661        | 132.0 | 660  | 0.1399          |
| 0.0619        | 136.0 | 680  | 0.1359          |
| 0.0617        | 140.0 | 700  | 0.1318          |
| 0.064         | 144.0 | 720  | 0.1316          |
| 0.0542        | 148.0 | 740  | 0.1309          |
| 0.0584        | 152.0 | 760  | 0.1286          |
| 0.0525        | 156.0 | 780  | 0.1298          |
| 0.069         | 160.0 | 800  | 0.1283          |
| 0.05          | 164.0 | 820  | 0.1270          |
| 0.0497        | 168.0 | 840  | 0.1240          |
| 0.0478        | 172.0 | 860  | 0.1231          |
| 0.0472        | 176.0 | 880  | 0.1190          |
| 0.0457        | 180.0 | 900  | 0.1207          |
| 0.0435        | 184.0 | 920  | 0.1221          |
| 0.0427        | 188.0 | 940  | 0.1212          |
| 0.0425        | 192.0 | 960  | 0.1197          |
| 0.0481        | 196.0 | 980  | 0.1199          |
| 0.0484        | 200.0 | 1000 | 0.1210          |
| 0.0553        | 204.0 | 1020 | 0.1197          |
| 0.0389        | 208.0 | 1040 | 0.1177          |
| 0.0391        | 212.0 | 1060 | 0.1181          |
| 0.0491        | 216.0 | 1080 | 0.1226          |
| 0.038         | 220.0 | 1100 | 0.1189          |
| 0.0443        | 224.0 | 1120 | 0.1177          |
| 0.038         | 228.0 | 1140 | 0.1178          |
| 0.0365        | 232.0 | 1160 | 0.1186          |
| 0.044         | 236.0 | 1180 | 0.1174          |
| 0.0608        | 240.0 | 1200 | 0.1205          |
| 0.0356        | 244.0 | 1220 | 0.1200          |
| 0.0431        | 248.0 | 1240 | 0.1195          |
| 0.0414        | 252.0 | 1260 | 0.1183          |
| 0.0374        | 256.0 | 1280 | 0.1173          |
| 0.0389        | 260.0 | 1300 | 0.1185          |
| 0.0335        | 264.0 | 1320 | 0.1181          |
| 0.0363        | 268.0 | 1340 | 0.1182          |
| 0.0408        | 272.0 | 1360 | 0.1191          |
| 0.0411        | 276.0 | 1380 | 0.1186          |
| 0.037         | 280.0 | 1400 | 0.1176          |
| 0.0424        | 284.0 | 1420 | 0.1185          |
| 0.04          | 288.0 | 1440 | 0.1200          |
| 0.0399        | 292.0 | 1460 | 0.1187          |
| 0.0415        | 296.0 | 1480 | 0.1186          |
| 0.038         | 300.0 | 1500 | 0.1170          |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.1+cpu
- Datasets 2.19.2
- Tokenizers 0.19.1