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-ve-U11-b-40
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: validation
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.8260869565217391

swin-tiny-patch4-window7-224-ve-U11-b-40

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: 0.6121
Accuracy: 0.8261

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: 40

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.92	6	1.5799	0.4783
2.1773	2.0	13	1.5648	0.3478
2.1773	2.92	19	1.5182	0.3261
2.1773	4.0	26	1.4232	0.3261
1.8993	4.92	32	1.3505	0.3913
1.8993	6.0	39	1.2747	0.3696
1.5045	6.92	45	1.2452	0.3696
1.2431	8.0	52	1.1982	0.2826
1.2431	8.92	58	1.2112	0.3043
1.1225	10.0	65	1.0160	0.5
0.9942	10.92	71	1.0138	0.4783
0.9942	12.0	78	0.9094	0.5652
0.9212	12.92	84	0.8860	0.5217
0.816	14.0	91	0.7693	0.6739
0.816	14.92	97	0.8290	0.6304
0.741	16.0	104	0.7810	0.6739
0.631	16.92	110	0.6342	0.7826
0.631	18.0	117	0.7677	0.6957
0.6402	18.92	123	0.6283	0.7391
0.5477	20.0	130	0.6687	0.7174
0.5477	20.92	136	0.6369	0.7826
0.5023	22.0	143	0.6334	0.7609
0.5023	22.92	149	0.6355	0.8043
0.4802	24.0	156	0.5976	0.8043
0.4336	24.92	162	0.6112	0.7609
0.4336	26.0	169	0.6148	0.8043
0.4203	26.92	175	0.6380	0.7391
0.429	28.0	182	0.6032	0.8043
0.429	28.92	188	0.6348	0.7391
0.4013	30.0	195	0.6121	0.8261
0.3747	30.92	201	0.6521	0.7391
0.3747	32.0	208	0.6424	0.7609
0.3668	32.92	214	0.6149	0.8261
0.3287	34.0	221	0.6426	0.7826
0.3287	34.92	227	0.6379	0.8043
0.372	36.0	234	0.6435	0.8043
0.3236	36.92	240	0.6450	0.8043

Framework versions

Transformers 4.36.2
Pytorch 2.1.2+cu118
Datasets 2.16.1
Tokenizers 0.15.0