metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- food101
metrics:
- accuracy
base_model: microsoft/swin-base-patch4-window7-224
model-index:
- name: swin-finetuned-food101
results:
- task:
type: image-classification
name: Image Classification
dataset:
name: food101
type: food101
args: default
metrics:
- type: accuracy
value: 0.9210297029702971
name: Accuracy
- task:
type: image-classification
name: Image Classification
dataset:
name: food101
type: food101
config: default
split: validation
metrics:
- type: accuracy
value: 0.9135841584158416
name: Accuracy
verified: true
- type: precision
value: 0.9151645786633058
name: Precision Macro
verified: true
- type: precision
value: 0.9135841584158416
name: Precision Micro
verified: true
- type: precision
value: 0.915164578663306
name: Precision Weighted
verified: true
- type: recall
value: 0.9135841584158414
name: Recall Macro
verified: true
- type: recall
value: 0.9135841584158416
name: Recall Micro
verified: true
- type: recall
value: 0.9135841584158416
name: Recall Weighted
verified: true
- type: f1
value: 0.9138785016966742
name: F1 Macro
verified: true
- type: f1
value: 0.9135841584158415
name: F1 Micro
verified: true
- type: f1
value: 0.9138785016966743
name: F1 Weighted
verified: true
- type: loss
value: 0.30761435627937317
name: loss
verified: true
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: 0.2772
- Accuracy: 0.9210
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: 3
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
0.5077 | 1.0 | 1183 | 0.3851 | 0.8893 |
0.3523 | 2.0 | 2366 | 0.3124 | 0.9088 |
0.1158 | 3.0 | 3549 | 0.2772 | 0.9210 |
Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1