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c4fed05
metadata
license: apache-2.0
base_model: facebook/convnextv2-tiny-22k-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: convnextv2-tiny-22k-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

convnextv2-tiny-22k-224-finetuned-piid

This model is a fine-tuned version of facebook/convnextv2-tiny-22k-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6118
  • 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
1.2083 0.98 20 1.0137 0.6027
0.6826 2.0 41 0.6901 0.6895
0.5161 2.98 61 0.6377 0.7078
0.4475 4.0 82 0.5423 0.7215
0.4325 4.98 102 0.5165 0.7671
0.3433 6.0 123 0.5916 0.7763
0.2677 6.98 143 0.5866 0.7534
0.2498 8.0 164 0.5146 0.7900
0.2387 8.98 184 0.5631 0.7580
0.2132 10.0 205 0.5320 0.7991
0.2178 10.98 225 0.5833 0.7854
0.1474 12.0 246 0.5902 0.7900
0.1627 12.98 266 0.6142 0.7808
0.1651 14.0 287 0.6063 0.7808
0.158 14.98 307 0.6130 0.7808
0.126 16.0 328 0.6647 0.7671
0.0821 16.98 348 0.5972 0.7808
0.1062 18.0 369 0.5975 0.7945
0.1031 18.98 389 0.6129 0.7808
0.1268 19.51 400 0.6118 0.7854

Framework versions

  • Transformers 4.33.3
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.5
  • Tokenizers 0.13.3