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-wuhan
    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: 0.4

swin-tiny-patch4-window7-224-finetuned-wuhan

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: 0.7953
Accuracy: 0.4

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: 64
eval_batch_size: 64
seed: 42
gradient_accumulation_steps: 2
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: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	3	0.7953	0.4
No log	2.0	6	0.9477	0.4
No log	3.0	9	1.0106	0.4
0.5883	4.0	12	1.4170	0.4
0.5883	5.0	15	1.7436	0.4
0.5883	6.0	18	2.5380	0.4
0.241	7.0	21	3.8803	0.4
0.241	8.0	24	2.4040	0.2222
0.241	9.0	27	3.9968	0.4
0.125	10.0	30	3.2731	0.4
0.125	11.0	33	3.2202	0.2222
0.125	12.0	36	4.7008	0.4
0.125	13.0	39	4.5588	0.3556
0.0766	14.0	42	4.5434	0.2444
0.0766	15.0	45	4.9792	0.2667
0.0766	16.0	48	5.4095	0.2667
0.0239	17.0	51	5.8507	0.2222
0.0239	18.0	54	6.1023	0.2222
0.0239	19.0	57	6.1666	0.2222
0.0129	20.0	60	6.1948	0.2222

Framework versions

Transformers 4.31.0
Pytorch 2.0.1+cu118
Datasets 2.14.1
Tokenizers 0.13.3