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---
library_name: transformers
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: swin-tiny-patch4-window7-224-image-classifier
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: 0.748792270531401
- name: F1
type: f1
value: 0.655421686746988
- name: Precision
type: precision
value: 0.6267281105990783
- name: Recall
type: recall
value: 0.6868686868686869
---
<!-- 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. -->
# swin-tiny-patch4-window7-224-image-classifier
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4362
- Accuracy: 0.7488
- F1: 0.6554
- Precision: 0.6267
- Recall: 0.6869
## 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: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.5982 | 1.0 | 143 | 0.5693 | 0.6711 | 0.4144 | 0.5441 | 0.3346 |
| 0.4391 | 2.0 | 286 | 0.4924 | 0.7295 | 0.4849 | 0.7178 | 0.3662 |
| 0.3658 | 3.0 | 429 | 0.4332 | 0.7501 | 0.6459 | 0.6368 | 0.6553 |
| 0.3404 | 4.0 | 572 | 0.4202 | 0.7694 | 0.6525 | 0.6857 | 0.6225 |
| 0.3188 | 5.0 | 715 | 0.4362 | 0.7488 | 0.6554 | 0.6267 | 0.6869 |
### Framework versions
- Transformers 4.46.0
- Pytorch 2.5.0+cu121
- Datasets 3.0.2
- Tokenizers 0.20.1
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