---
license: apache-2.0
tags:
- image-classification
- vision
- generated_from_trainer
datasets:
- food101
metrics:
- accuracy
model-index:
- name: jpqd-swin-b-15eph-r1.00-s2e5-mock-main-merge-pr2
  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: 0.9144158415841585
---

<!-- 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. -->

# jpqd-swin-b-15eph-r1.00-s2e5-mock-main-merge-pr2

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: 0.2970
- Accuracy: 0.9144

## 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: 15.0

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 3.8787        | 0.42  | 500   | 3.9971          | 0.7163   |
| 0.8429        | 0.84  | 1000  | 0.6450          | 0.8678   |
| 0.8561        | 1.27  | 1500  | 0.4160          | 0.8945   |
| 0.5777        | 1.69  | 2000  | 0.3664          | 0.9006   |
| 12.3601       | 2.11  | 2500  | 12.0328         | 0.9023   |
| 49.0606       | 2.54  | 3000  | 48.5000         | 0.8526   |
| 75.3173       | 2.96  | 3500  | 75.5341         | 0.6942   |
| 93.6153       | 3.38  | 4000  | 93.3091         | 0.5929   |
| 103.5744      | 3.8   | 4500  | 103.1211        | 0.5846   |
| 107.7701      | 4.23  | 5000  | 108.0755        | 0.5398   |
| 109.5736      | 4.65  | 5500  | 108.7624        | 0.5855   |
| 1.8028        | 5.07  | 6000  | 1.0960          | 0.8179   |
| 1.2549        | 5.49  | 6500  | 0.6560          | 0.8695   |
| 0.7199        | 5.92  | 7000  | 0.5619          | 0.8769   |
| 0.8874        | 6.34  | 7500  | 0.5151          | 0.8859   |
| 0.7429        | 6.76  | 8000  | 0.4830          | 0.8898   |
| 0.6759        | 7.19  | 8500  | 0.4681          | 0.8926   |
| 0.5352        | 7.61  | 9000  | 0.4360          | 0.8956   |
| 0.6021        | 8.03  | 9500  | 0.4202          | 0.8979   |
| 0.5617        | 8.45  | 10000 | 0.3940          | 0.9003   |
| 0.7235        | 8.88  | 10500 | 0.3915          | 0.9000   |
| 0.5323        | 9.3   | 11000 | 0.3793          | 0.9017   |
| 0.589         | 9.72  | 11500 | 0.3670          | 0.9051   |
| 0.425         | 10.14 | 12000 | 0.3615          | 0.9059   |
| 0.7103        | 10.57 | 12500 | 0.3479          | 0.9070   |
| 0.6251        | 10.99 | 13000 | 0.3472          | 0.9073   |
| 0.623         | 11.41 | 13500 | 0.3353          | 0.9088   |
| 0.6012        | 11.83 | 14000 | 0.3292          | 0.9098   |
| 0.4984        | 12.26 | 14500 | 0.3230          | 0.9112   |
| 0.4763        | 12.68 | 15000 | 0.3158          | 0.9109   |
| 0.3209        | 13.1  | 15500 | 0.3120          | 0.9123   |
| 0.4854        | 13.52 | 16000 | 0.3057          | 0.9126   |
| 0.5472        | 13.95 | 16500 | 0.3032          | 0.9134   |
| 0.3264        | 14.37 | 17000 | 0.3013          | 0.9134   |
| 0.4136        | 14.79 | 17500 | 0.2977          | 0.9141   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2