---
license: other
base_model: nvidia/mit-b5
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b5-miic-tl
  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-miic-tl

This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the yijisuk/ic-chip-sample dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2028
- eval_mean_iou: 0.3885
- eval_mean_accuracy: 0.7770
- eval_overall_accuracy: 0.7770
- eval_accuracy_unlabeled: nan
- eval_accuracy_circuit: 0.7770
- eval_iou_unlabeled: 0.0
- eval_iou_circuit: 0.7770
- eval_dice_coefficient: 0.7854
- eval_runtime: 1.8601
- eval_samples_per_second: 5.376
- eval_steps_per_second: 2.688
- epoch: 48.75
- step: 3900

## 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 4.36.2
- Pytorch 1.11.0+cu115
- Datasets 2.15.0
- Tokenizers 0.15.0