---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224-pt22k-ft22k
tags:
- image-segmentation
- vision
- generated_from_trainer
model-index:
- name: beit-base-patch16-224-pt22k-ft22k-finetuned-plantorgans
  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. -->

# beit-base-patch16-224-pt22k-ft22k-finetuned-plantorgans

This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the jpodivin/plantorgans dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2004
- Mean Iou: 0.0260
- Mean Accuracy: 0.0325
- Overall Accuracy: 0.0435
- Accuracy Void: nan
- Accuracy Fruit: 0.1299
- Accuracy Leaf: 0.0
- Accuracy Flower: 0.0
- Accuracy Stem: 0.0001
- Iou Void: 0.0
- Iou Fruit: 0.1299
- Iou Leaf: 0.0
- Iou Flower: 0.0
- Iou Stem: 0.0001

## 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: 5e-05
- 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
- lr_scheduler_warmup_steps: 100
- num_epochs: 10.0

### 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------:|:--------------:|:-------------:|:---------------:|:-------------:|:--------:|:---------:|:--------:|:----------:|:--------:|
| 1.0657        | 1.0   | 575  | 1.1178          | 0.0158   | 0.0197        | 0.0261           | nan           | 0.0776         | 0.0           | 0.0             | 0.0012        | 0.0      | 0.0776    | 0.0      | 0.0        | 0.0012   |
| 0.5398        | 2.0   | 1150 | 1.0816          | 0.0361   | 0.0451        | 0.0599           | nan           | 0.1783         | 0.0000        | 0.0             | 0.0020        | 0.0      | 0.1783    | 0.0000   | 0.0        | 0.0020   |
| 0.4794        | 3.0   | 1725 | 1.2122          | 0.0110   | 0.0138        | 0.0179           | nan           | 0.0517         | 0.0003        | 0.0             | 0.0032        | 0.0      | 0.0517    | 0.0003   | 0.0        | 0.0032   |
| 0.446         | 4.0   | 2300 | 1.3138          | 0.0114   | 0.0142        | 0.0189           | nan           | 0.0562         | 0.0           | 0.0             | 0.0006        | 0.0      | 0.0562    | 0.0      | 0.0        | 0.0006   |
| 0.4422        | 5.0   | 2875 | 1.2360          | 0.0005   | 0.0006        | 0.0006           | nan           | 0.0012         | 0.0           | 0.0             | 0.0013        | 0.0      | 0.0012    | 0.0      | 0.0        | 0.0013   |
| 0.4183        | 6.0   | 3450 | 1.3598          | 0.0177   | 0.0221        | 0.0296           | nan           | 0.0885         | 0.0           | 0.0             | 0.0           | 0.0      | 0.0885    | 0.0      | 0.0        | 0.0      |
| 0.3921        | 7.0   | 4025 | 1.2523          | 0.0267   | 0.0333        | 0.0446           | nan           | 0.1333         | 0.0000        | 0.0             | 0.0           | 0.0      | 0.1333    | 0.0000   | 0.0        | 0.0      |
| 0.3743        | 8.0   | 4600 | 1.3146          | 0.0401   | 0.0502        | 0.0670           | nan           | 0.2002         | 0.0           | 0.0             | 0.0005        | 0.0      | 0.2001    | 0.0      | 0.0        | 0.0005   |
| 0.3695        | 9.0   | 5175 | 1.2873          | 0.0294   | 0.0368        | 0.0492           | nan           | 0.1472         | 0.0           | 0.0             | 0.0001        | 0.0      | 0.1471    | 0.0      | 0.0        | 0.0001   |
| 0.3796        | 10.0  | 5750 | 1.2004          | 0.0260   | 0.0325        | 0.0435           | nan           | 0.1299         | 0.0           | 0.0             | 0.0001        | 0.0      | 0.1299    | 0.0      | 0.0        | 0.0001   |


### Framework versions

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