metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- food101
metrics:
- accuracy
model-index:
- name: swin-finetuned-food101
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: food101
type: food101
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9220198019801981
swin-finetuned-food101
This model is a fine-tuned version of microsoft/swin-base-patch4-window7-224 on the food101 dataset. It achieves the following results on the evaluation set:
- Loss: 0.4401
- Accuracy: 0.9220
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: 16
- 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
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0579 | 1.0 | 1183 | 0.4190 | 0.9102 |
| 0.0129 | 2.0 | 2366 | 0.4179 | 0.9155 |
| 0.0076 | 3.0 | 3549 | 0.4219 | 0.9198 |
| 0.0197 | 4.0 | 4732 | 0.4487 | 0.9160 |
| 0.0104 | 5.0 | 5915 | 0.4414 | 0.9210 |
| 0.0007 | 6.0 | 7098 | 0.4401 | 0.9220 |
| 0.0021 | 7.0 | 8281 | 0.4401 | 0.9220 |
| 0.0015 | 8.0 | 9464 | 0.4401 | 0.9220 |
| 0.0056 | 9.0 | 10647 | 0.4401 | 0.9220 |
| 0.0019 | 10.0 | 11830 | 0.4401 | 0.9220 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2