metadata

license: apache-2.0
base_model: microsoft/swin-small-patch4-window7-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: swin-small-patch4-window7-224-finetuned-piid
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: val
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.776255707762557

swin-small-patch4-window7-224-finetuned-piid

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

Loss: 0.6168
Accuracy: 0.7763

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: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
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
1.2327	0.98	20	1.1687	0.5114
0.7354	2.0	41	0.7696	0.6712
0.602	2.98	61	0.7198	0.7078
0.5809	4.0	82	0.5824	0.7397
0.4989	4.98	102	0.5331	0.7489
0.4364	6.0	123	0.6137	0.7489
0.3321	6.98	143	0.5839	0.7717
0.3	8.0	164	0.5246	0.7763
0.3024	8.98	184	0.5557	0.7717
0.3433	10.0	205	0.5258	0.7900
0.258	10.98	225	0.6354	0.7489
0.1595	12.0	246	0.5492	0.8219
0.2295	12.98	266	0.5889	0.7900
0.1956	14.0	287	0.5670	0.7900
0.2028	14.98	307	0.5460	0.7900
0.1514	16.0	328	0.6587	0.7900
0.0934	16.98	348	0.6131	0.7945
0.1323	18.0	369	0.6615	0.7900
0.1213	18.98	389	0.6192	0.7671
0.1028	19.51	400	0.6168	0.7763

Framework versions

Transformers 4.35.0
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.14.1