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-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: 0.7853881278538812

swin-tiny-patch4-window7-224-finetuned-piid

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: 0.5715
Accuracy: 0.7854

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
1.2088	0.98	20	1.1661	0.4521
0.7545	2.0	41	0.8866	0.6073
0.6281	2.98	61	0.7788	0.6849
0.5939	4.0	82	0.6443	0.7397
0.5254	4.98	102	0.5097	0.7808
0.5583	6.0	123	0.5715	0.7854
0.3463	6.98	143	0.6163	0.7352
0.3878	8.0	164	0.5671	0.7671
0.3653	8.98	184	0.5690	0.7580
0.3529	10.0	205	0.5940	0.7580
0.301	10.98	225	0.6303	0.7626
0.2639	12.0	246	0.5725	0.7763
0.2847	12.98	266	0.6280	0.7717
0.25	14.0	287	0.5975	0.7717
0.2472	14.98	307	0.5821	0.7671
0.1676	16.0	328	0.6456	0.7626
0.1327	16.98	348	0.6117	0.7671
0.1977	18.0	369	0.6988	0.7489
0.1602	18.98	389	0.6448	0.7671
0.1785	19.51	400	0.6333	0.7717

Framework versions

Transformers 4.35.0
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.14.1