metadata

license: apache-2.0
base_model: microsoft/swinv2-base-patch4-window8-256
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: swinv2-base-patch4-window8-256
    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: 0.7241379310344828

swinv2-base-patch4-window8-256

This model is a fine-tuned version of microsoft/swinv2-base-patch4-window8-256 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.6211
Accuracy: 0.7241

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: 0.0001
train_batch_size: 1
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 4
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.5428	0.9912	28	0.6211	0.7241
0.6494	1.9823	56	0.6130	0.7241
0.5752	2.9735	84	0.6846	0.7241
0.7165	4.0	113	0.9642	0.7241
0.5699	4.9912	141	0.6072	0.7241
0.5517	5.9823	169	0.6231	0.7241
0.5268	6.9735	197	0.6098	0.7241
0.672	8.0	226	0.5891	0.7241
0.5448	8.9912	254	0.6023	0.7241
0.555	9.9823	282	0.5917	0.7241
0.5818	10.9735	310	0.5940	0.7241
0.6556	12.0	339	0.5966	0.7241
0.716	12.9912	367	0.5904	0.7241
0.6104	13.9823	395	0.5938	0.7241
0.5046	14.9735	423	0.5921	0.7241
0.5871	16.0	452	0.6027	0.7241
0.5222	16.9912	480	0.5921	0.7241
0.5511	17.9823	508	0.5948	0.7241
0.6394	18.9735	536	0.5969	0.7241
0.566	20.0	565	0.6005	0.7241
0.6032	20.9912	593	0.5968	0.7241
0.4824	21.9823	621	0.5934	0.7241
0.4975	22.9735	649	0.5979	0.7241
0.4976	24.0	678	0.6034	0.7241
0.5355	24.9912	706	0.6033	0.7241
0.4323	25.9823	734	0.6015	0.7241
0.5579	26.9735	762	0.6043	0.7241
0.5639	28.0	791	0.6023	0.7241
0.5595	28.9912	819	0.5996	0.7241
0.4372	29.7345	840	0.5995	0.7241

Framework versions

Transformers 4.42.3
Pytorch 2.3.1+cu118
Datasets 2.20.0
Tokenizers 0.19.1