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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
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: 0.9055049504950495
---
<!-- 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
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 food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3497
- Accuracy: 0.9055
This model is quantized. Structured sparsity in transformer linear layers: 40%.
## 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 2.2676 | 0.42 | 500 | 2.1087 | 0.7947 |
| 0.6823 | 0.84 | 1000 | 0.5127 | 0.8818 |
| 0.816 | 1.27 | 1500 | 0.3944 | 0.8954 |
| 0.5272 | 1.69 | 2000 | 0.3310 | 0.9050 |
| 12.263 | 2.11 | 2500 | 12.0040 | 0.9057 |
| 48.9519 | 2.54 | 3000 | 48.4500 | 0.8597 |
| 75.576 | 2.96 | 3500 | 75.5765 | 0.6951 |
| 93.7523 | 3.38 | 4000 | 93.3753 | 0.5992 |
| 103.7155 | 3.8 | 4500 | 103.5301 | 0.5622 |
| 107.7993 | 4.23 | 5000 | 108.0881 | 0.5636 |
| 109.6831 | 4.65 | 5500 | 109.2205 | 0.5844 |
| 1.8848 | 5.07 | 6000 | 0.9807 | 0.8315 |
| 1.0668 | 5.49 | 6500 | 0.6050 | 0.8740 |
| 0.7951 | 5.92 | 7000 | 0.5151 | 0.8838 |
| 0.7402 | 6.34 | 7500 | 0.4843 | 0.8906 |
| 0.7319 | 6.76 | 8000 | 0.4494 | 0.8933 |
| 0.5683 | 7.19 | 8500 | 0.4378 | 0.8953 |
| 0.496 | 7.61 | 9000 | 0.4115 | 0.8981 |
| 0.6174 | 8.03 | 9500 | 0.3952 | 0.9005 |
| 0.4921 | 8.45 | 10000 | 0.3765 | 0.9026 |
| 0.5843 | 8.88 | 10500 | 0.3678 | 0.9035 |
| 0.5485 | 9.3 | 11000 | 0.3576 | 0.9039 |
| 0.4337 | 9.72 | 11500 | 0.3512 | 0.9057 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
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