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---
license: apache-2.0
base_model: microsoft/swin-base-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: swin-finetuned-food101
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: 0.4166666666666667
- name: F1
type: f1
value: 0.5882352941176471
- name: Precision
type: precision
value: 0.4166666666666667
- name: Recall
type: recall
value: 1.0
---
<!-- 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-finetuned-food101
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co./microsoft/swin-base-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7046
- Accuracy: 0.4167
- F1: 0.5882
- Precision: 0.4167
- Recall: 1.0
- Auc: 0.5742
## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|
| 0.6978 | 1.0 | 14 | 0.6847 | 0.5833 | 0.0 | 0.0 | 0.0 | 0.5717 |
| 0.7025 | 2.0 | 28 | 0.7120 | 0.4167 | 0.5882 | 0.4167 | 1.0 | 0.5570 |
| 0.6946 | 3.0 | 42 | 0.6955 | 0.4167 | 0.5882 | 0.4167 | 1.0 | 0.5662 |
| 0.6935 | 4.0 | 56 | 0.7047 | 0.4167 | 0.5882 | 0.4167 | 1.0 | 0.5644 |
| 0.6935 | 5.0 | 70 | 0.7046 | 0.4167 | 0.5882 | 0.4167 | 1.0 | 0.5742 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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