metadata

license: apache-2.0
base_model: microsoft/swin-base-patch4-window7-224-in22k
tags:
  - generated_from_trainer
datasets:
  - food101
metrics:
  - accuracy
model-index:
  - name: swin-base-patch4-window7-224-in22k-food101-16-7
    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: 0.9292277227722773

swin-base-patch4-window7-224-in22k-food101-16-7

This model is a fine-tuned version of microsoft/swin-base-patch4-window7-224-in22k on the food101 dataset. It achieves the following results on the evaluation set:

Loss: 0.2515
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: 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: 7

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.8296	1.0	1183	0.4354	0.8731
0.6811	2.0	2367	0.3406	0.8999
0.4531	3.0	3551	0.2902	0.9154
0.5265	4.0	4735	0.2751	0.9199
0.4338	5.0	5918	0.2689	0.9227
0.3443	6.0	7102	0.2538	0.9276
0.3871	7.0	8281	0.2515	0.9292

Framework versions

Transformers 4.34.1
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.14.1