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-sealv1
    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: 0.946969696969697

swin-tiny-patch4-window7-224-finetuned-sealv1

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: 0.4069
Accuracy: 0.9470

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	0.95	9	1.1667	0.6023
1.2777	2.0	19	0.9542	0.8864
0.8743	2.95	28	0.5694	0.9015
0.5282	4.0	38	0.3682	0.9129
0.2988	4.95	47	0.2135	0.9545
0.1832	6.0	57	0.2820	0.9167
0.1867	6.95	66	0.1944	0.9432
0.1077	8.0	76	0.2345	0.9432
0.0571	8.95	85	0.2389	0.9470
0.0379	10.0	95	0.2260	0.9432
0.0233	10.95	104	0.2329	0.9432
0.0163	12.0	114	0.2610	0.9356
0.019	12.95	123	0.3660	0.9508
0.0113	14.0	133	0.2777	0.9470
0.0084	14.95	142	0.3123	0.9508
0.008	16.0	152	0.3222	0.9470
0.0048	16.95	161	0.3232	0.9470
0.0075	18.0	171	0.3476	0.9508
0.0048	18.95	180	0.3304	0.9470
0.0143	20.0	190	0.4560	0.9432
0.0143	20.95	199	0.3720	0.9432
0.0019	22.0	209	0.3579	0.9394
0.0063	22.95	218	0.4064	0.9432
0.0023	24.0	228	0.4741	0.9394
0.0015	24.95	237	0.4111	0.9470
0.0022	26.0	247	0.3914	0.9432
0.0008	26.95	256	0.3945	0.9432
0.0024	28.0	266	0.4053	0.9470
0.0026	28.42	270	0.4069	0.9470

Framework versions

Transformers 4.38.2
Pytorch 1.10.2+cu113
Datasets 2.18.0
Tokenizers 0.15.2