metadata
license: afl-3.0
tags:
- generated_from_trainer
model-index:
- name: swinv2-tiny-patch4-window8-256-finetuned-THFOOD-50
results: []
datasets:
- thean/THFOOD-50
widget:
- src: >-
https://huggingface.co/datasets/thean/sample_images/resolve/main/FriedChicken.jpg
example_title: Fried Chicken
- src: >-
https://huggingface.co/datasets/thean/sample_images/resolve/main/Dumpling.jpg
example_title: Dumpling
- src: >-
https://huggingface.co/datasets/thean/sample_images/resolve/main/CurriedFishCake.jpg
example_title: Curried Fish Cake
- src: >-
https://huggingface.co/datasets/thean/sample_images/resolve/main/MasssamanGai.jpg
example_title: Masssaman Gai
- src: >-
https://huggingface.co/datasets/thean/sample_images/resolve/main/EggsStewed.jpg
example_title: Eggs Stewed
- src: >-
https://huggingface.co/datasets/thean/sample_images/resolve/main/KhanomJeenNamYaKati.jpg
example_title: Khanom Jeen Nam Ya Kati
- src: >-
https://huggingface.co/datasets/thean/sample_images/resolve/main/GaengJued.jpg
example_title: Gaeng Jued
metrics:
- accuracy
library_name: transformers
swinv2-tiny-patch4-window8-256-finetuned-THFOOD-50
This model is a fine-tuned version of microsoft/swinv2-tiny-patch4-window8-256 on the THFOOD-50 dataset.
It achieves the following results on the:
Train set
- Loss: 0.1669
- Accuracy: 0.9557
Validation set
- Loss: 0.2535
- Accuracy: 0.9344
Test set
- Loss: 0.2669
- Accuracy: 0.9292
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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- 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
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 3.6558 | 0.99 | 47 | 3.1956 | 0.28 |
| 1.705 | 1.99 | 94 | 1.1701 | 0.6787 |
| 0.9805 | 2.98 | 141 | 0.6492 | 0.8125 |
| 0.7925 | 4.0 | 189 | 0.4724 | 0.8644 |
| 0.6169 | 4.99 | 236 | 0.4129 | 0.8738 |
| 0.5343 | 5.99 | 283 | 0.3717 | 0.8825 |
| 0.5196 | 6.98 | 330 | 0.3654 | 0.8906 |
| 0.5059 | 8.0 | 378 | 0.3267 | 0.8969 |
| 0.4432 | 8.99 | 425 | 0.2996 | 0.9081 |
| 0.3819 | 9.99 | 472 | 0.3056 | 0.9087 |
| 0.3627 | 10.98 | 519 | 0.2796 | 0.9213 |
| 0.3505 | 12.0 | 567 | 0.2753 | 0.915 |
| 0.3224 | 12.99 | 614 | 0.2830 | 0.9206 |
| 0.3206 | 13.99 | 661 | 0.2797 | 0.9231 |
| 0.3141 | 14.98 | 708 | 0.2569 | 0.9287 |
| 0.2946 | 16.0 | 756 | 0.2582 | 0.9319 |
| 0.3008 | 16.99 | 803 | 0.2583 | 0.9337 |
| 0.2356 | 17.99 | 850 | 0.2567 | 0.9281 |
| 0.2954 | 18.98 | 897 | 0.2581 | 0.9319 |
| 0.2628 | 19.89 | 940 | 0.2535 | 0.9344 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3