---
license: mit
base_model: openmmlab/upernet-swin-small
tags:
- image-segmentation
- vision
- generated_from_trainer
model-index:
- name: upernet-swin-small-finetuned
  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. -->

# upernet-swin-small-finetuned

This model is a fine-tuned version of [openmmlab/upernet-swin-small](https://huggingface.co/openmmlab/upernet-swin-small) on the jpodivin/plantorgans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2914
- Mean Iou: 0.4182
- Mean Accuracy: 0.5282
- Overall Accuracy: 0.7341
- Accuracy Void: nan
- Accuracy Fruit: 0.8590
- Accuracy Leaf: 0.7032
- Accuracy Flower: 0.0
- Accuracy Stem: 0.5505
- Iou Void: 0.0
- Iou Fruit: 0.8554
- Iou Leaf: 0.6976
- Iou Flower: 0.0
- Iou Stem: 0.5381
- Median Iou: 0.5381

## 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: 0.0006
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Void | Accuracy Fruit | Accuracy Leaf | Accuracy Flower | Accuracy Stem | Iou Void | Iou Fruit | Iou Leaf | Iou Flower | Iou Stem | Median Iou |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------:|:--------------:|:-------------:|:---------------:|:-------------:|:--------:|:---------:|:--------:|:----------:|:--------:|:----------:|
| 0.8566        | 1.0   | 575  | 0.3365          | 0.3723   | 0.4705        | 0.6560           | nan           | 0.8000         | 0.6122        | 0.0             | 0.4699        | 0.0      | 0.7976    | 0.6041   | 0.0        | 0.4598   | 0.4598     |
| 0.3338        | 2.0   | 1150 | 0.3030          | 0.3922   | 0.4937        | 0.7155           | nan           | 0.8558         | 0.7024        | 0.0             | 0.4166        | 0.0      | 0.8517    | 0.6972   | 0.0        | 0.4119   | 0.4119     |
| 0.3477        | 3.0   | 1725 | 0.2914          | 0.4182   | 0.5282        | 0.7341           | nan           | 0.8590         | 0.7032        | 0.0             | 0.5505        | 0.0      | 0.8554    | 0.6976   | 0.0        | 0.5381   | 0.5381     |


### Framework versions

- Transformers 4.38.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0