metadata

license: apache-2.0
base_model: Yogesh1p/swin-tiny-patch4-window7-224-finetuned-cp1
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: swin-tiny-patch4-window7-224-finetuned-cp1
    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: 0.8666666666666667

swin-tiny-patch4-window7-224-finetuned-cp1

This model is a fine-tuned version of Yogesh1p/swin-tiny-patch4-window7-224-finetuned-cp1 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.3628
Accuracy: 0.8667

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	0.92	3	0.4305	0.8222
No log	1.85	6	0.4510	0.8
No log	2.77	9	0.4329	0.7778
0.3489	4.0	13	0.4965	0.7778
0.3489	4.92	16	0.4247	0.8
0.3489	5.85	19	0.3559	0.8444
0.2872	6.77	22	0.3628	0.8667
0.2872	8.0	26	0.3511	0.8667
0.2872	8.92	29	0.3538	0.8667
0.3071	9.85	32	0.3644	0.8222
0.3071	10.77	35	0.3860	0.8222
0.3071	12.0	39	0.5917	0.8
0.2765	12.92	42	0.5149	0.7778
0.2765	13.85	45	0.5605	0.7556
0.2765	14.77	48	0.4737	0.8
0.2637	16.0	52	0.4109	0.8444
0.2637	16.92	55	0.3584	0.8222
0.2637	17.85	58	0.3771	0.8222
0.2082	18.77	61	0.4406	0.8222
0.2082	20.0	65	0.3592	0.8222
0.2082	20.92	68	0.3302	0.8667
0.1944	21.85	71	0.3206	0.8444
0.1944	22.77	74	0.3215	0.8667
0.1944	24.0	78	0.3209	0.8667
0.2144	24.92	81	0.3327	0.8444
0.2144	25.85	84	0.3506	0.8444
0.2144	26.77	87	0.3545	0.8444
0.1754	27.69	90	0.3494	0.8444

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0