---
license: other
base_model: nvidia/mit-b1
tags:
- vision
- image-segmentation
- generated_from_trainer
datasets:
- kelp_data
model-index:
- name: segformer-b1-kelp-rgb-agg-imgaug-jan-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-b1-kelp-rgb-agg-imgaug-jan-22

This model is a fine-tuned version of [nvidia/mit-b1](https://huggingface.co/nvidia/mit-b1) on the samitizerxu/kelp_data dataset.
It achieves the following results on the evaluation set:
- eval_accuracy_kelp: nan
- eval_iou_kelp: 0.0
- eval_loss: 0.3223
- eval_mean_iou: 0.0205
- eval_mean_accuracy: 0.0410
- eval_overall_accuracy: 0.0410
- eval_runtime: 62.0057
- eval_samples_per_second: 27.272
- eval_steps_per_second: 3.419
- epoch: 1.16
- step: 570

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 40

### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0