---
license: other
base_model: nvidia/mit-b0
tags:
- generated_from_trainer
model-index:
- name: segformer-webots-grasp
  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-webots-grasp

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.6628
- eval_mean_iou: 0.8546
- eval_mean_accuracy: 0.8633
- eval_overall_accuracy: 0.8633
- eval_per_category_iou: [0.8545649439735425, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
- eval_per_category_accuracy: [0.8632884477610067, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
- eval_runtime: 53.0339
- eval_samples_per_second: 0.396
- eval_steps_per_second: 0.207
- epoch: 2.6
- step: 104

## 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

### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0