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internal.swin-base-food101-int8-structured38.01

Image Classification
Transformers
PyTorch
OpenVINO
swin
vision
generated_from_trainer
Eval Results
Inference Endpoints
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internal.swin-base-food101-int8-structured38.01 / README.md
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metadata
license: apache-2.0
tags:
  - image-classification
  - vision
  - generated_from_trainer
datasets:
  - food101
metrics:
  - accuracy
model-index:
  - name: swin-food101-jpqd-1to2r1.5-epo10-finetuned-student
    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: 0.9183762376237624

swin-food101-jpqd-1to2r1.5-epo10-finetuned-student

This model is a fine-tuned version of skylord/swin-finetuned-food101 on the food101 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2391
  • Accuracy: 0.9184

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
  • num_epochs: 10.0

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.3011 0.42 500 0.1951 0.9124
0.2613 0.84 1000 0.1897 0.9139
100.1552 1.27 1500 99.5975 0.7445
162.0751 1.69 2000 162.5020 0.3512
1.061 2.11 2500 0.7523 0.8550
0.9728 2.54 3000 0.5263 0.8767
0.5851 2.96 3500 0.4599 0.8892
0.4668 3.38 4000 0.4064 0.8938
0.6967 3.8 4500 0.3814 0.8986
0.4928 4.23 5000 0.3522 0.9036
0.4893 4.65 5500 0.3562 0.9026
0.5421 5.07 6000 0.3182 0.9049
0.4405 5.49 6500 0.3112 0.9071
0.4423 5.92 7000 0.3012 0.9092
0.4143 6.34 7500 0.2958 0.9095
0.4997 6.76 8000 0.2796 0.9126
0.2448 7.19 8500 0.2747 0.9124
0.4468 7.61 9000 0.2699 0.9144
0.4163 8.03 9500 0.2583 0.9166
0.3651 8.45 10000 0.2567 0.9165
0.3946 8.88 10500 0.2489 0.9176
0.3196 9.3 11000 0.2444 0.9180
0.312 9.72 11500 0.2402 0.9172

Framework versions

  • Transformers 4.26.0
  • Pytorch 1.13.1+cu116
  • Datasets 2.8.0
  • Tokenizers 0.13.2