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swin-base-food101-jpqd-ov

Image Classification
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OpenVINO
swin
vision
generated_from_trainer
Eval Results
Inference Endpoints
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metadata
license: apache-2.0
tags:
  - image-classification
  - vision
  - generated_from_trainer
datasets:
  - food101
metrics:
  - accuracy
model-index:
  - name: swin-base-food101-jpqd-ov
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: food101
          type: food101
          config: default
          split: validation
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9060990099009901

swin-base-food101-jpqd-ov

It was compressed using NNCF with Optimum Intel following the JPQD image classification example.

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: 0.3396
  • Accuracy: 0.9061

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
2.2162 0.42 500 2.1111 0.7967
0.729 0.84 1000 0.5474 0.8773
0.7536 1.27 1500 0.3844 0.8984
0.4822 1.69 2000 0.3340 0.9043
12.2559 2.11 2500 12.0128 0.9033
48.7302 2.54 3000 48.3874 0.8681
75.1831 2.96 3500 75.3200 0.7183
93.5572 3.38 4000 93.4142 0.5939
103.798 3.8 4500 103.4427 0.5634
108.0993 4.23 5000 108.6461 0.5490
110.1265 4.65 5500 109.3663 0.5636
1.5584 5.07 6000 0.9255 0.8374
1.0883 5.49 6500 0.5841 0.8758
0.7024 5.92 7000 0.5055 0.8854
0.9033 6.34 7500 0.4639 0.8901
0.6901 6.76 8000 0.4360 0.8947
0.6114 7.19 8500 0.4080 0.8978
0.5102 7.61 9000 0.3911 0.9009
0.7154 8.03 9500 0.3747 0.9027
0.5621 8.45 10000 0.3622 0.9021
0.5262 8.88 10500 0.3554 0.9041
0.5442 9.3 11000 0.3462 0.9053
0.5615 9.72 11500 0.3416 0.9061

Framework versions

  • Transformers 4.26.1
  • Pytorch 1.13.1+cu117
  • Datasets 2.8.0
  • Tokenizers 0.13.2