transformers safetensors segformer vision image-segmentation generated_from_trainer base_model:nvidia/mit-b5 license:other endpoints_compatible region:us
---
license: other
base_model: nvidia/mit-b5
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b5-seed42-outputs
  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-b5-seed42-outputs

This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the unreal-hug/REAL_DATASET_SEG_401_6_lbls dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2833
- Mean Iou: 0.3430
- Mean Accuracy: 0.4050
- Overall Accuracy: 0.6546
- Accuracy Unlabeled: nan
- Accuracy Lv: 0.7625
- Accuracy Rv: 0.6171
- Accuracy Ra: 0.7072
- Accuracy La: 0.6623
- Accuracy Vs: 0.0
- Accuracy As: 0.0
- Accuracy Mk: 0.0227
- Accuracy Tk: nan
- Accuracy Asd: 0.3003
- Accuracy Vsd: 0.4268
- Accuracy Ak: 0.5517
- Iou Unlabeled: 0.0
- Iou Lv: 0.7175
- Iou Rv: 0.5629
- Iou Ra: 0.6665
- Iou La: 0.5980
- Iou Vs: 0.0
- Iou As: 0.0
- Iou Mk: 0.0207
- Iou Tk: nan
- Iou Asd: 0.2802
- Iou Vsd: 0.3970
- Iou Ak: 0.5307

## 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: 0.0001
- 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: cosine
- lr_scheduler_warmup_ratio: 0.05
- training_steps: 1000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Lv | Accuracy Rv | Accuracy Ra | Accuracy La | Accuracy Vs | Accuracy As | Accuracy Mk | Accuracy Tk | Accuracy Asd | Accuracy Vsd | Accuracy Ak | Iou Unlabeled | Iou Lv | Iou Rv | Iou Ra | Iou La | Iou Vs | Iou As | Iou Mk | Iou Tk | Iou Asd | Iou Vsd | Iou Ak |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:------------:|:------------:|:-----------:|:-------------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:-------:|:-------:|:------:|
| 0.4921        | 0.62  | 100  | 0.4897          | 0.0906   | 0.1245        | 0.3551           | nan                | 0.7217      | 0.0098      | 0.0751      | 0.4344      | 0.0         | 0.0         | 0.0         | nan         | 0.0          | 0.0          | 0.0043      | 0.0           | 0.5846 | 0.0097 | 0.0746 | 0.3230 | 0.0    | 0.0    | 0.0    | nan    | 0.0     | 0.0     | 0.0043 |
| 0.3534        | 1.25  | 200  | 0.3565          | 0.2420   | 0.3017        | 0.5592           | nan                | 0.7947      | 0.4293      | 0.5941      | 0.3492      | 0.0         | 0.0         | 0.0         | nan         | 0.1737       | 0.1500       | 0.5262      | 0.0           | 0.7343 | 0.3850 | 0.3997 | 0.3289 | 0.0    | 0.0    | 0.0    | nan    | 0.1681  | 0.1456  | 0.5007 |
| 0.4663        | 1.88  | 300  | 0.3434          | 0.3620   | 0.4497        | 0.7082           | nan                | 0.8026      | 0.7564      | 0.6551      | 0.7392      | 0.0         | 0.0         | 0.0         | nan         | 0.3118       | 0.5764       | 0.6560      | 0.0           | 0.7545 | 0.6572 | 0.6039 | 0.5921 | 0.0    | 0.0    | 0.0    | nan    | 0.2819  | 0.4908  | 0.6019 |
| 0.1737        | 2.5   | 400  | 0.3055          | 0.3331   | 0.4090        | 0.6394           | nan                | 0.7469      | 0.6281      | 0.5765      | 0.6122      | 0.0         | 0.0         | 0.0004      | nan         | 0.2401       | 0.6135       | 0.6724      | 0.0           | 0.7075 | 0.5704 | 0.5194 | 0.5310 | 0.0    | 0.0    | 0.0003 | nan    | 0.2292  | 0.5279  | 0.5789 |
| 0.1954        | 3.12  | 500  | 0.3052          | 0.2570   | 0.2980        | 0.5174           | nan                | 0.6624      | 0.4973      | 0.4223      | 0.5361      | 0.0         | 0.0         | 0.0022      | nan         | 0.1117       | 0.3193       | 0.4284      | 0.0           | 0.6289 | 0.4592 | 0.4113 | 0.4902 | 0.0    | 0.0    | 0.0022 | nan    | 0.1107  | 0.3024  | 0.4216 |
| 0.2666        | 3.75  | 600  | 0.3177          | 0.3808   | 0.4720        | 0.7175           | nan                | 0.7675      | 0.7191      | 0.8483      | 0.7341      | 0.0         | 0.0         | 0.0950      | nan         | 0.3086       | 0.6065       | 0.6405      | 0.0           | 0.7200 | 0.6353 | 0.6912 | 0.6409 | 0.0    | 0.0    | 0.0845 | nan    | 0.2905  | 0.5245  | 0.6024 |
| 0.2214        | 4.38  | 700  | 0.2971          | 0.3748   | 0.4463        | 0.7178           | nan                | 0.8524      | 0.6207      | 0.7488      | 0.7353      | 0.0         | 0.0         | 0.025       | nan         | 0.3236       | 0.5440       | 0.6130      | 0.0           | 0.7909 | 0.5707 | 0.6987 | 0.6564 | 0.0    | 0.0    | 0.0235 | nan    | 0.3015  | 0.4902  | 0.5907 |
| 0.2624        | 5.0   | 800  | 0.2833          | 0.3430   | 0.4050        | 0.6546           | nan                | 0.7625      | 0.6171      | 0.7072      | 0.6623      | 0.0         | 0.0         | 0.0227      | nan         | 0.3003       | 0.4268       | 0.5517      | 0.0           | 0.7175 | 0.5629 | 0.6665 | 0.5980 | 0.0    | 0.0    | 0.0207 | nan    | 0.2802  | 0.3970  | 0.5307 |
| 0.3578        | 5.62  | 900  | 0.2847          | 0.3329   | 0.3926        | 0.6257           | nan                | 0.7276      | 0.5712      | 0.6573      | 0.6410      | 0.0016      | 0.0         | 0.0227      | nan         | 0.3125       | 0.4450       | 0.5470      | 0.0           | 0.6860 | 0.5210 | 0.6234 | 0.5790 | 0.0015 | 0.0    | 0.0210 | nan    | 0.2906  | 0.4122  | 0.5275 |
| 0.2736        | 6.25  | 1000 | 0.2861          | 0.3393   | 0.4010        | 0.6425           | nan                | 0.7587      | 0.5808      | 0.6702      | 0.6477      | 0.0014      | 0.0         | 0.0244      | nan         | 0.3087       | 0.4702       | 0.5477      | 0.0           | 0.7133 | 0.5292 | 0.6319 | 0.5844 | 0.0014 | 0.0    | 0.0225 | nan    | 0.2877  | 0.4328  | 0.5295 |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
