transformers tensorboard safetensors segformer vision image-segmentation generated_from_trainer base_model:nvidia/mit-b0 license:other endpoints_compatible region:us
---
license: other
base_model: nvidia/mit-b0
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b0-finetuned-segments-sidewalk-oct-22
  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-oct-22

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the julia-wenkmann/TennisSegmentation dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0577
- Mean Iou: 0.1977
- Mean Accuracy: 0.2635
- Overall Accuracy: 0.5273
- Accuracy Undefined: nan
- Accuracy Ball: 0.0
- Accuracy Playertop: 0.1196
- Accuracy Playerbottom: 0.6710
- Iou Undefined: 0.0
- Iou Ball: 0.0
- Iou Playertop: 0.1196
- Iou Playerbottom: 0.6710

## 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 Undefined | Accuracy Ball | Accuracy Playertop | Accuracy Playerbottom | Iou Undefined | Iou Ball | Iou Playertop | Iou Playerbottom |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:------------------:|:---------------------:|:-------------:|:--------:|:-------------:|:----------------:|
| 1.0528        | 1.67  | 20   | 1.1784          | 0.1882   | 0.2528        | 0.5396           | nan                | 0.0           | 0.0471             | 0.7115                | 0.0           | 0.0      | 0.0411        | 0.7115           |
| 0.8484        | 3.33  | 40   | 0.8579          | 0.1435   | 0.1914        | 0.4261           | nan                | 0.0           | 0.0                | 0.5741                | 0.0           | 0.0      | 0.0           | 0.5741           |
| 0.6695        | 5.0   | 60   | 0.6303          | 0.1414   | 0.1885        | 0.4196           | nan                | 0.0           | 0.0                | 0.5654                | 0.0           | 0.0      | 0.0           | 0.5654           |
| 0.5808        | 6.67  | 80   | 0.5370          | 0.0901   | 0.1201        | 0.2674           | nan                | 0.0           | 0.0                | 0.3603                | 0.0           | 0.0      | 0.0           | 0.3603           |
| 0.4415        | 8.33  | 100  | 0.4385          | 0.1263   | 0.1685        | 0.3751           | nan                | 0.0           | 0.0                | 0.5054                | 0.0           | 0.0      | 0.0           | 0.5054           |
| 0.3955        | 10.0  | 120  | 0.3449          | 0.1177   | 0.1570        | 0.3496           | nan                | 0.0           | 0.0                | 0.4710                | 0.0           | 0.0      | 0.0           | 0.4710           |
| 0.3597        | 11.67 | 140  | 0.3006          | 0.1186   | 0.1582        | 0.3521           | nan                | 0.0           | 0.0                | 0.4745                | 0.0           | 0.0      | 0.0           | 0.4745           |
| 0.2761        | 13.33 | 160  | 0.2592          | 0.0968   | 0.1290        | 0.2873           | nan                | 0.0           | 0.0                | 0.3871                | 0.0           | 0.0      | 0.0           | 0.3871           |
| 0.2343        | 15.0  | 180  | 0.2044          | 0.0987   | 0.1316        | 0.2930           | nan                | 0.0           | 0.0                | 0.3948                | 0.0           | 0.0      | 0.0           | 0.3948           |
| 0.1953        | 16.67 | 200  | 0.1841          | 0.1258   | 0.1678        | 0.3736           | nan                | 0.0           | 0.0                | 0.5033                | 0.0           | 0.0      | 0.0           | 0.5033           |
| 0.1676        | 18.33 | 220  | 0.1558          | 0.1394   | 0.1858        | 0.4137           | nan                | 0.0           | 0.0                | 0.5574                | 0.0           | 0.0      | 0.0           | 0.5574           |
| 0.1486        | 20.0  | 240  | 0.1392          | 0.1448   | 0.1931        | 0.4299           | nan                | 0.0           | 0.0                | 0.5792                | 0.0           | 0.0      | 0.0           | 0.5792           |
| 0.1281        | 21.67 | 260  | 0.1234          | 0.1497   | 0.1996        | 0.4445           | nan                | 0.0           | 0.0                | 0.5989                | 0.0           | 0.0      | 0.0           | 0.5989           |
| 0.1138        | 23.33 | 280  | 0.1064          | 0.1407   | 0.1877        | 0.4178           | nan                | 0.0           | 0.0                | 0.5630                | 0.0           | 0.0      | 0.0           | 0.5630           |
| 0.1031        | 25.0  | 300  | 0.0952          | 0.1495   | 0.1993        | 0.4438           | nan                | 0.0           | 0.0                | 0.5979                | 0.0           | 0.0      | 0.0           | 0.5979           |
| 0.094         | 26.67 | 320  | 0.0896          | 0.1500   | 0.2000        | 0.4454           | nan                | 0.0           | 0.0                | 0.6001                | 0.0           | 0.0      | 0.0           | 0.6001           |
| 0.0873        | 28.33 | 340  | 0.0889          | 0.1677   | 0.2236        | 0.4978           | nan                | 0.0           | 0.0                | 0.6707                | 0.0           | 0.0      | 0.0           | 0.6707           |
| 0.0822        | 30.0  | 360  | 0.0772          | 0.1631   | 0.2175        | 0.4843           | nan                | 0.0           | 0.0                | 0.6526                | 0.0           | 0.0      | 0.0           | 0.6526           |
| 0.0769        | 31.67 | 380  | 0.0739          | 0.1589   | 0.2119        | 0.4718           | nan                | 0.0           | 0.0                | 0.6358                | 0.0           | 0.0      | 0.0           | 0.6358           |
| 0.0798        | 33.33 | 400  | 0.0694          | 0.1603   | 0.2137        | 0.4758           | nan                | 0.0           | 0.0                | 0.6411                | 0.0           | 0.0      | 0.0           | 0.6411           |
| 0.0704        | 35.0  | 420  | 0.0654          | 0.1680   | 0.2241        | 0.4910           | nan                | 0.0           | 0.0158             | 0.6564                | 0.0           | 0.0      | 0.0158        | 0.6564           |
| 0.0653        | 36.67 | 440  | 0.0633          | 0.1708   | 0.2278        | 0.4957           | nan                | 0.0           | 0.0229             | 0.6604                | 0.0           | 0.0      | 0.0229        | 0.6604           |
| 0.0648        | 38.33 | 460  | 0.0610          | 0.1743   | 0.2324        | 0.5013           | nan                | 0.0           | 0.0325             | 0.6648                | 0.0           | 0.0      | 0.0325        | 0.6648           |
| 0.0616        | 40.0  | 480  | 0.0598          | 0.1859   | 0.2479        | 0.5189           | nan                | 0.0           | 0.0664             | 0.6773                | 0.0           | 0.0      | 0.0664        | 0.6773           |
| 0.0612        | 41.67 | 500  | 0.0586          | 0.1887   | 0.2517        | 0.5203           | nan                | 0.0           | 0.0805             | 0.6745                | 0.0           | 0.0      | 0.0805        | 0.6745           |
| 0.0693        | 43.33 | 520  | 0.0584          | 0.1953   | 0.2604        | 0.5300           | nan                | 0.0           | 0.1003             | 0.6810                | 0.0           | 0.0      | 0.1003        | 0.6810           |
| 0.0595        | 45.0  | 540  | 0.0567          | 0.1998   | 0.2664        | 0.5354           | nan                | 0.0           | 0.1161             | 0.6831                | 0.0           | 0.0      | 0.1161        | 0.6831           |
| 0.0564        | 46.67 | 560  | 0.0556          | 0.2007   | 0.2676        | 0.5378           | nan                | 0.0           | 0.1165             | 0.6862                | 0.0           | 0.0      | 0.1165        | 0.6862           |
| 0.0608        | 48.33 | 580  | 0.0555          | 0.2032   | 0.2710        | 0.5412           | nan                | 0.0           | 0.1249             | 0.6880                | 0.0           | 0.0      | 0.1249        | 0.6880           |
| 0.0599        | 50.0  | 600  | 0.0577          | 0.1977   | 0.2635        | 0.5273           | nan                | 0.0           | 0.1196             | 0.6710                | 0.0           | 0.0      | 0.1196        | 0.6710           |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
