metadata
license: apache-2.0
tags:
- image-classification
- vision
- generated_from_trainer
datasets:
- food101
metrics:
- accuracy
model-index:
- name: swin-food101-jpqd-1to2r1.5-epo7-finetuned-student
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: 0.9123960396039604
swin-food101-jpqd-1to2r1.5-epo7-finetuned-student
This model is a fine-tuned version of skylord/swin-finetuned-food101 on the food101 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2658
- Accuracy: 0.9124
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
- num_epochs: 7.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.2977 | 0.42 | 500 | 0.1949 | 0.9112 |
| 0.3183 | 0.84 | 1000 | 0.1867 | 0.9144 |
| 99.9552 | 1.27 | 1500 | 99.4882 | 0.7577 |
| 162.4195 | 1.69 | 2000 | 162.7763 | 0.3373 |
| 1.2272 | 2.11 | 2500 | 0.7333 | 0.8564 |
| 1.0236 | 2.54 | 3000 | 0.5016 | 0.8823 |
| 0.6472 | 2.96 | 3500 | 0.4337 | 0.8908 |
| 0.52 | 3.38 | 4000 | 0.3927 | 0.8974 |
| 0.6075 | 3.8 | 4500 | 0.3506 | 0.9011 |
| 0.5348 | 4.23 | 5000 | 0.3425 | 0.9006 |
| 0.444 | 4.65 | 5500 | 0.3268 | 0.9044 |
| 0.5787 | 5.07 | 6000 | 0.3020 | 0.9078 |
| 0.3995 | 5.49 | 6500 | 0.2932 | 0.9095 |
| 0.414 | 5.92 | 7000 | 0.2806 | 0.9104 |
| 0.4386 | 6.34 | 7500 | 0.2738 | 0.9112 |
| 0.452 | 6.76 | 8000 | 0.2673 | 0.9127 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2