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---
license: apache-2.0
base_model: microsoft/swin-base-patch4-window7-224-in22k
tags:
- generated_from_trainer
datasets:
- food101
metrics:
- accuracy
model-index:
- name: swin-base-patch4-window7-224-in22k-food101-24-12
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: food101
type: food101
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9312475247524753
---
<!-- 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-base-patch4-window7-224-in22k-food101-24-12
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2524
- Accuracy: 0.9312
## 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: 24
- eval_batch_size: 24
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 96
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8657 | 1.0 | 789 | 0.4698 | 0.8663 |
| 0.7506 | 2.0 | 1578 | 0.3419 | 0.9006 |
| 0.6379 | 3.0 | 2367 | 0.3061 | 0.9116 |
| 0.5223 | 4.0 | 3157 | 0.2906 | 0.9149 |
| 0.4989 | 5.0 | 3946 | 0.2783 | 0.9205 |
| 0.4163 | 6.0 | 4735 | 0.2732 | 0.9225 |
| 0.3954 | 7.0 | 5524 | 0.2675 | 0.9255 |
| 0.3466 | 8.0 | 6314 | 0.2710 | 0.9240 |
| 0.3666 | 9.0 | 7103 | 0.2625 | 0.9275 |
| 0.2085 | 10.0 | 7892 | 0.2578 | 0.9295 |
| 0.263 | 11.0 | 8681 | 0.2563 | 0.9302 |
| 0.2171 | 12.0 | 9468 | 0.2524 | 0.9312 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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