metadata
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: finetuned-SwinT-Indian-Food-Classification-v2
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: Indian-Food-Images
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9458023379383634
finetuned-SwinT-Indian-Food-Classification-v2
This model is a fine-tuned version of microsoft/swin-base-patch4-window7-224-in22k on the Indian-Food-Images dataset. It achieves the following results on the evaluation set:
- Loss: 0.2226
- Accuracy: 0.9458
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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.9351 | 0.3 | 100 | 0.6017 | 0.8363 |
| 0.5667 | 0.6 | 200 | 0.4384 | 0.8767 |
| 0.5548 | 0.9 | 300 | 0.4215 | 0.8767 |
| 0.5516 | 1.2 | 400 | 0.4290 | 0.8735 |
| 0.3782 | 1.5 | 500 | 0.3502 | 0.8980 |
| 0.3115 | 1.8 | 600 | 0.3780 | 0.8937 |
| 0.4229 | 2.1 | 700 | 0.3545 | 0.8905 |
| 0.3832 | 2.4 | 800 | 0.3446 | 0.9086 |
| 0.2745 | 2.7 | 900 | 0.3299 | 0.9150 |
| 0.2063 | 3.0 | 1000 | 0.2592 | 0.9277 |
| 0.2077 | 3.3 | 1100 | 0.3772 | 0.9150 |
| 0.2041 | 3.6 | 1200 | 0.2855 | 0.9214 |
| 0.2541 | 3.9 | 1300 | 0.2502 | 0.9330 |
| 0.1203 | 4.2 | 1400 | 0.2577 | 0.9362 |
| 0.1594 | 4.5 | 1500 | 0.2226 | 0.9458 |
| 0.1015 | 4.8 | 1600 | 0.2368 | 0.9437 |
Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1