metadata

license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: swin-tiny-patch4-window7-224-finetuned-phones
    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: 0.8653846153846154

swin-tiny-patch4-window7-224-finetuned-phones

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

Loss: 0.3938
Accuracy: 0.8654

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: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.9333	7	0.6743	0.5673
0.6763	2.0	15	0.6166	0.6923
0.635	2.9333	22	0.5646	0.7404
0.5724	4.0	30	0.5074	0.7308
0.5724	4.9333	37	0.4809	0.7692
0.527	6.0	45	0.4597	0.7692
0.5304	6.9333	52	0.4758	0.7596
0.4597	8.0	60	0.4343	0.7885
0.4597	8.9333	67	0.4249	0.7981
0.4606	10.0	75	0.4236	0.7981
0.4286	10.9333	82	0.4055	0.8462
0.3857	12.0	90	0.4144	0.8269
0.3857	12.9333	97	0.4294	0.7981
0.3801	14.0	105	0.4081	0.8462
0.3538	14.9333	112	0.4195	0.8462
0.3585	16.0	120	0.4069	0.8558
0.3585	16.9333	127	0.3971	0.8558
0.3258	18.0	135	0.3938	0.8654
0.3288	18.9333	142	0.3964	0.8462
0.3276	20.0	150	0.4423	0.8558
0.3276	20.9333	157	0.4067	0.8365
0.317	22.0	165	0.4179	0.8654
0.288	22.9333	172	0.3882	0.8558
0.2735	24.0	180	0.4215	0.8558
0.2735	24.9333	187	0.3972	0.8462
0.2805	26.0	195	0.3943	0.8558
0.2961	26.9333	202	0.3999	0.8558
0.2832	28.0	210	0.4043	0.8558

Framework versions

Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.19.2
Tokenizers 0.19.1