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---
license: apache-2.0
tags:
- image-classification
- vision
- generated_from_trainer
datasets:
- food101
metrics:
- accuracy
model-index:
- name: jpqd-swin-b-20eph-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: 0.9179009900990099
---
<!-- 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-20eph-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: 0.2715
- Accuracy: 0.9179
## 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: 20.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 5.1452 | 0.42 | 500 | 5.4928 | 0.6440 |
| 0.9839 | 0.84 | 1000 | 0.7956 | 0.8580 |
| 0.8533 | 1.27 | 1500 | 0.4392 | 0.8911 |
| 0.6123 | 1.69 | 2000 | 0.3768 | 0.8983 |
| 12.3076 | 2.11 | 2500 | 12.0798 | 0.8953 |
| 49.301 | 2.54 | 3000 | 48.6292 | 0.8343 |
| 75.6345 | 2.96 | 3500 | 75.7027 | 0.6777 |
| 94.2556 | 3.38 | 4000 | 93.5852 | 0.5604 |
| 103.3226 | 3.8 | 4500 | 103.1255 | 0.5702 |
| 107.3423 | 4.23 | 5000 | 107.9250 | 0.5359 |
| 108.9013 | 4.65 | 5500 | 108.5225 | 0.5882 |
| 2.045 | 5.07 | 6000 | 1.1149 | 0.8154 |
| 1.3377 | 5.49 | 6500 | 0.6747 | 0.8665 |
| 0.7565 | 5.92 | 7000 | 0.5814 | 0.8765 |
| 0.7493 | 6.34 | 7500 | 0.5460 | 0.8840 |
| 0.7693 | 6.76 | 8000 | 0.5109 | 0.8851 |
| 0.6082 | 7.19 | 8500 | 0.4893 | 0.8895 |
| 0.7575 | 7.61 | 9000 | 0.4521 | 0.8943 |
| 0.7943 | 8.03 | 9500 | 0.4465 | 0.8941 |
| 0.5521 | 8.45 | 10000 | 0.4119 | 0.8967 |
| 0.6536 | 8.88 | 10500 | 0.4071 | 0.9010 |
| 0.5164 | 9.3 | 11000 | 0.3945 | 0.9010 |
| 0.6687 | 9.72 | 11500 | 0.3884 | 0.9030 |
| 0.4374 | 10.14 | 12000 | 0.3764 | 0.9040 |
| 0.7326 | 10.57 | 12500 | 0.3678 | 0.9060 |
| 0.6148 | 10.99 | 13000 | 0.3602 | 0.9057 |
| 0.6068 | 11.41 | 13500 | 0.3566 | 0.9075 |
| 0.6105 | 11.83 | 14000 | 0.3456 | 0.9074 |
| 0.5277 | 12.26 | 14500 | 0.3383 | 0.9107 |
| 0.5255 | 12.68 | 15000 | 0.3328 | 0.9097 |
| 0.4536 | 13.1 | 15500 | 0.3268 | 0.9108 |
| 0.5337 | 13.52 | 16000 | 0.3256 | 0.9107 |
| 0.5299 | 13.95 | 16500 | 0.3161 | 0.9124 |
| 0.3037 | 14.37 | 17000 | 0.3162 | 0.9123 |
| 0.4171 | 14.79 | 17500 | 0.3078 | 0.9124 |
| 0.5375 | 15.22 | 18000 | 0.3002 | 0.9116 |
| 0.2722 | 15.64 | 18500 | 0.2953 | 0.9134 |
| 0.3684 | 16.06 | 19000 | 0.2960 | 0.9137 |
| 0.4369 | 16.48 | 19500 | 0.2918 | 0.9150 |
| 0.3346 | 16.91 | 20000 | 0.2856 | 0.9171 |
| 0.3645 | 17.33 | 20500 | 0.2856 | 0.9162 |
| 0.4475 | 17.75 | 21000 | 0.2833 | 0.9157 |
| 0.2553 | 18.17 | 21500 | 0.2788 | 0.9167 |
| 0.5098 | 18.6 | 22000 | 0.2766 | 0.9164 |
| 0.4149 | 19.02 | 22500 | 0.2732 | 0.9177 |
| 0.3737 | 19.44 | 23000 | 0.2734 | 0.9181 |
| 0.325 | 19.86 | 23500 | 0.2715 | 0.9176 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2
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