metadata

license: apache-2.0
base_model: microsoft/swinv2-tiny-patch4-window16-256
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: swinv2-tiny-patch4-window16-256-prueba3
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.717948717948718

swinv2-tiny-patch4-window16-256-prueba3

This model is a fine-tuned version of microsoft/swinv2-tiny-patch4-window16-256 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.5599
Accuracy: 0.7179

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: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
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
No log	0.9091	5	0.6165	0.6667
0.6601	2.0	11	0.5599	0.7179
0.6601	2.7273	15	0.5462	0.7179

Framework versions

Transformers 4.40.2
Pytorch 2.2.1+cu121
Datasets 2.19.1
Tokenizers 0.19.1