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swin-food101-jpqd

This model is a fine-tuned version of microsoft/swin-base-patch4-window7-224 on the food101 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3497
  • Accuracy: 0.9055

This model is quantized. Structured sparsity in transformer linear layers: 40%.

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: 16
  • eval_batch_size: 128
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 10.0

Training results

Training Loss Epoch Step Validation Loss Accuracy
2.2676 0.42 500 2.1087 0.7947
0.6823 0.84 1000 0.5127 0.8818
0.816 1.27 1500 0.3944 0.8954
0.5272 1.69 2000 0.3310 0.9050
12.263 2.11 2500 12.0040 0.9057
48.9519 2.54 3000 48.4500 0.8597
75.576 2.96 3500 75.5765 0.6951
93.7523 3.38 4000 93.3753 0.5992
103.7155 3.8 4500 103.5301 0.5622
107.7993 4.23 5000 108.0881 0.5636
109.6831 4.65 5500 109.2205 0.5844
1.8848 5.07 6000 0.9807 0.8315
1.0668 5.49 6500 0.6050 0.8740
0.7951 5.92 7000 0.5151 0.8838
0.7402 6.34 7500 0.4843 0.8906
0.7319 6.76 8000 0.4494 0.8933
0.5683 7.19 8500 0.4378 0.8953
0.496 7.61 9000 0.4115 0.8981
0.6174 8.03 9500 0.3952 0.9005
0.4921 8.45 10000 0.3765 0.9026
0.5843 8.88 10500 0.3678 0.9035
0.5485 9.3 11000 0.3576 0.9039
0.4337 9.72 11500 0.3512 0.9057

Framework versions

  • Transformers 4.26.0
  • Pytorch 1.13.1+cu116
  • Datasets 2.8.0
  • Tokenizers 0.13.2
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Dataset used to train yujiepan/internal.swin-base-food101-int8-structured40

Evaluation results