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---
license: apache-2.0
base_model: microsoft/swinv2-large-patch4-window12to24-192to384-22kto1k-ft
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swinv2-large-patch4-window12to24-192to384-22kto1k-ft-microbes-merged
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7268518518518519
---
<!-- 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. -->
# swinv2-large-patch4-window12to24-192to384-22kto1k-ft-microbes-merged
This model is a fine-tuned version of [microsoft/swinv2-large-patch4-window12to24-192to384-22kto1k-ft](https://huggingface.co/microsoft/swinv2-large-patch4-window12to24-192to384-22kto1k-ft) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8626
- Accuracy: 0.7269
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.8355 | 0.98 | 15 | 2.5831 | 0.3333 |
| 1.9292 | 1.97 | 30 | 1.6850 | 0.5046 |
| 1.4121 | 2.95 | 45 | 1.2324 | 0.5972 |
| 1.0121 | 4.0 | 61 | 1.0345 | 0.6852 |
| 0.854 | 4.98 | 76 | 0.9663 | 0.6806 |
| 0.701 | 5.97 | 91 | 0.9587 | 0.6991 |
| 0.5956 | 6.95 | 106 | 0.8626 | 0.7269 |
| 0.5713 | 7.87 | 120 | 0.8645 | 0.7222 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cpu
- Datasets 2.14.4
- Tokenizers 0.13.3
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