---
license: other
base_model: nvidia/mit-b0
tags:
- image-segmentation
- vision
- generated_from_trainer
model-index:
- name: segformer-finetuned-ihc
  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-finetuned-ihc

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the Isaacks/ihc_slide_tissue dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0326
- eval_mean_iou: 0.0
- eval_mean_accuracy: nan
- eval_overall_accuracy: nan
- eval_accuracy_background: nan
- eval_accuracy_tissue: nan
- eval_iou_background: 0.0
- eval_iou_tissue: 0.0
- eval_runtime: 19.1281
- eval_samples_per_second: 0.784
- eval_steps_per_second: 0.105
- epoch: 9.15
- step: 183

## 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: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: polynomial
- training_steps: 10000

### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.2
- Tokenizers 0.13.3