vit-weld-classify / README.md
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metadata
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - image-classification
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: vit-weld-classify
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.6894977168949772

vit-weld-classify

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7966
  • Accuracy: 0.6895

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: 0.0002
  • train_batch_size: 16
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 18
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.8686 0.8130 100 0.7966 0.6895
0.6935 1.6260 200 1.2217 0.5068
0.4225 2.4390 300 0.9592 0.6210
0.2586 3.2520 400 1.3123 0.5936
0.237 4.0650 500 0.8075 0.6986
0.2658 4.8780 600 1.0878 0.6210
0.1904 5.6911 700 1.1048 0.7169
0.0964 6.5041 800 1.3602 0.6849
0.0474 7.3171 900 1.1331 0.7671
0.1179 8.1301 1000 1.1228 0.7306
0.0447 8.9431 1100 1.2609 0.7397
0.0043 9.7561 1200 1.1746 0.7763
0.1059 10.5691 1300 1.1867 0.7763
0.0026 11.3821 1400 1.2890 0.7534
0.0039 12.1951 1500 1.3283 0.7580
0.002 13.0081 1600 1.1871 0.7671
0.0019 13.8211 1700 1.1643 0.7900
0.0264 14.6341 1800 1.1537 0.7900
0.0015 15.4472 1900 1.1821 0.7945
0.0015 16.2602 2000 1.1962 0.7900
0.0014 17.0732 2100 1.2036 0.7900
0.0014 17.8862 2200 1.2067 0.7900

Framework versions

  • Transformers 4.41.1
  • Pytorch 2.3.0+cu121
  • Datasets 2.19.1
  • Tokenizers 0.19.1