jayanta / swin-base-patch4-window7-224-20epochs-finetuned-memes

huggingface.co
Total runs: 14
24-hour runs: 0
7-day runs: -4
30-day runs: -74
Model's Last Updated: September 17 2022
image-classification

Introduction of swin-base-patch4-window7-224-20epochs-finetuned-memes

Model Details of swin-base-patch4-window7-224-20epochs-finetuned-memes

swin-base-patch4-window7-224-20epochs-finetuned-memes

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: 0.7090
  • Accuracy: 0.8478
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: 0.00012
  • 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: 20
Training results
Training Loss Epoch Step Validation Loss Accuracy
1.0238 0.99 40 0.9636 0.6445
0.777 1.99 80 0.6591 0.7666
0.4763 2.99 120 0.5381 0.8130
0.3215 3.99 160 0.5244 0.8253
0.2179 4.99 200 0.5123 0.8238
0.1868 5.99 240 0.5052 0.8308
0.154 6.99 280 0.5444 0.8338
0.1166 7.99 320 0.6318 0.8238
0.1099 8.99 360 0.5656 0.8338
0.0925 9.99 400 0.6057 0.8338
0.0779 10.99 440 0.5942 0.8393
0.0629 11.99 480 0.6112 0.8400
0.0742 12.99 520 0.6588 0.8331
0.0752 13.99 560 0.6143 0.8408
0.0577 14.99 600 0.6450 0.8516
0.0589 15.99 640 0.6787 0.8400
0.0555 16.99 680 0.6641 0.8454
0.052 17.99 720 0.7213 0.8524
0.0589 18.99 760 0.6917 0.8470
0.0506 19.99 800 0.7090 0.8478
Framework versions
  • Transformers 4.22.1
  • Pytorch 1.12.1+cu113
  • Datasets 2.4.0
  • Tokenizers 0.12.1

Runs of jayanta swin-base-patch4-window7-224-20epochs-finetuned-memes on huggingface.co

14
Total runs
0
24-hour runs
0
3-day runs
-4
7-day runs
-74
30-day runs

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