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---
license: apache-2.0
base_model: microsoft/swinv2-tiny-patch4-window8-256
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swinv2-tiny-patch4-window8-256-Ocular-Toxoplasmosis
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.08064516129032258
---
<!-- 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-tiny-patch4-window8-256-Ocular-Toxoplasmosis
This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co./microsoft/swinv2-tiny-patch4-window8-256) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 8.8834
- Accuracy: 0.0806
## 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: 40
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.73 | 2 | 8.8834 | 0.0806 |
| No log | 1.82 | 5 | 8.8522 | 0.0806 |
| No log | 2.91 | 8 | 8.7000 | 0.0806 |
| 8.7803 | 4.0 | 11 | 8.2692 | 0.0806 |
| 8.7803 | 4.73 | 13 | 7.8836 | 0.0806 |
| 8.7803 | 5.82 | 16 | 7.3279 | 0.0806 |
| 8.7803 | 6.91 | 19 | 6.7700 | 0.0806 |
| 7.5847 | 8.0 | 22 | 6.1880 | 0.0806 |
| 7.5847 | 8.73 | 24 | 5.7783 | 0.0806 |
| 7.5847 | 9.82 | 27 | 5.2113 | 0.0806 |
| 5.7442 | 10.91 | 30 | 4.7163 | 0.0806 |
| 5.7442 | 12.0 | 33 | 4.2648 | 0.0806 |
| 5.7442 | 12.73 | 35 | 3.9892 | 0.0806 |
| 5.7442 | 13.82 | 38 | 3.6134 | 0.0806 |
| 4.1747 | 14.91 | 41 | 3.2828 | 0.0806 |
| 4.1747 | 16.0 | 44 | 2.9957 | 0.0806 |
| 4.1747 | 16.73 | 46 | 2.8259 | 0.0806 |
| 4.1747 | 17.82 | 49 | 2.5988 | 0.0806 |
| 3.0458 | 18.91 | 52 | 2.4004 | 0.0806 |
| 3.0458 | 20.0 | 55 | 2.2272 | 0.0806 |
| 3.0458 | 20.73 | 57 | 2.1254 | 0.0806 |
| 2.3301 | 21.82 | 60 | 1.9937 | 0.0806 |
| 2.3301 | 22.91 | 63 | 1.8860 | 0.0806 |
| 2.3301 | 24.0 | 66 | 1.8005 | 0.0806 |
| 2.3301 | 24.73 | 68 | 1.7551 | 0.0806 |
| 1.9107 | 25.82 | 71 | 1.7021 | 0.0806 |
| 1.9107 | 26.91 | 74 | 1.6654 | 0.0806 |
| 1.9107 | 28.0 | 77 | 1.6434 | 0.0806 |
| 1.9107 | 28.73 | 79 | 1.6362 | 0.0806 |
| 1.7061 | 29.09 | 80 | 1.6348 | 0.0806 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
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