---
license: other
base_model: nvidia/mit-b0
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: exemple2-finetuned-segments
  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. -->

# exemple2-finetuned-segments

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset.

## 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: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou                                                                                                                                                                                                                                                                                                                                                                                                              | Per Category Accuracy                                                                                                                                                                                                                                                                                                                                                                                                     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| No log        | 1.0   | 5    | 3.5279          | 0.0125   | 0.0416        | 0.1024           | [0.003071310796148687, 0.1616174857459709, 0.0, 0.0, 0.0022739959938265523, 0.0, 0.0005909869482034998, 0.0017997990408432924, 0.0, 0.05474664289453949, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.09700429854115147, 0.0, 0.04184948021660935, 0.0006682389937106918, 0.0, 0.0, 0.00415553197202368, 0.004015113955619657, 0.0, 0.0, 6.202127329674078e-05, 0.05336540352809607, 0.0, 0.0, 0.0, 0.00025955435304111186, 0.0, 0.0] | [0.0032144853023359787, 0.18635872416872554, nan, 0.0, 0.0038356619632496504, nan, 0.0010970621048716994, 0.008956864181701621, nan, 0.06383171787997526, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.11043176613738219, nan, 0.051045006388159, 0.02862641327880683, nan, nan, 0.34138655462184875, 0.049846125151543415, 0.0, nan, 6.220517339692085e-05, 0.10777804260728432, 0.0, 0.0, 0.0, 0.0002737135463322385, 0.0, nan] |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2