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---
license: apache-2.0
tags:
- image-classification
- vision
- generated_from_trainer
datasets:
- food101
metrics:
- accuracy
model-index:
- name: swin-food101-jpqd-1to2r1-epo7-finetuned-student
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: 0.9213069306930693
---
<!-- 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-food101-jpqd-1to2r1-epo7-finetuned-student
This model is a fine-tuned version of [skylord/swin-finetuned-food101](https://huggingface.co/skylord/swin-finetuned-food101) on the food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1947
- Accuracy: 0.9213
## 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: 16
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2342 | 0.42 | 500 | 0.1993 | 0.9099 |
| 0.2891 | 0.84 | 1000 | 0.1912 | 0.9137 |
| 67.4995 | 1.27 | 1500 | 66.4760 | 0.8035 |
| 109.8398 | 1.69 | 2000 | 109.5154 | 0.4499 |
| 0.6337 | 2.11 | 2500 | 0.4865 | 0.8826 |
| 0.6605 | 2.54 | 3000 | 0.3551 | 0.9013 |
| 0.4013 | 2.96 | 3500 | 0.3176 | 0.9044 |
| 0.3949 | 3.38 | 4000 | 0.2839 | 0.9079 |
| 0.4632 | 3.8 | 4500 | 0.2652 | 0.9118 |
| 0.3717 | 4.23 | 5000 | 0.2459 | 0.9147 |
| 0.3308 | 4.65 | 5500 | 0.2439 | 0.9159 |
| 0.4232 | 5.07 | 6000 | 0.2259 | 0.9169 |
| 0.3426 | 5.49 | 6500 | 0.2147 | 0.9199 |
| 0.331 | 5.92 | 7000 | 0.2086 | 0.9189 |
| 0.3032 | 6.34 | 7500 | 0.2036 | 0.9201 |
| 0.3393 | 6.76 | 8000 | 0.1978 | 0.9204 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
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