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metadata
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - f1
  - recall
  - precision
model-index:
  - name: dit-base-Business_Documents_Classified_v2
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: data
          split: train
          args: data
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.826
language:
  - en
pipeline_tag: image-classification

dit-base-Business_Documents_Classified_v2

This model is a fine-tuned version of microsoft/dit-base on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6715
  • Accuracy: 0.826
  • Weighted f1: 0.8272
  • Micro f1: 0.826
  • Macro f1: 0.8242
  • Weighted recall: 0.826
  • Micro recall: 0.826
  • Macro recall: 0.8237
  • Weighted precision: 0.8327
  • Micro precision: 0.826
  • Macro precision: 0.8293

Model description

This is a classification model of 16 different types of documents.

For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Document%20AI/Multiclass%20Classification/Real%20World%20Documents%20Collections/Real%20World%20Documents%20Collections_v2.ipynb

Intended uses & limitations

This model is intended to demonstrate my ability to solve a complex problem using technology.

Training and evaluation data

Dataset Source: https://www.kaggle.com/datasets/shaz13/real-world-documents-collections

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 18

Training results

Training Loss Epoch Step Validation Loss Accuracy Weighted f1 Micro f1 Macro f1 Weighted recall Micro recall Macro recall Weighted precision Micro precision Macro precision
2.7266 0.99 31 2.4738 0.208 0.1811 0.208 0.1827 0.208 0.208 0.2101 0.2143 0.208 0.2246
2.171 1.98 62 1.8510 0.423 0.3936 0.4230 0.3925 0.423 0.423 0.4243 0.4503 0.423 0.4446
1.6525 2.98 93 1.2633 0.61 0.5884 0.61 0.5855 0.61 0.61 0.6124 0.6377 0.61 0.6283
1.346 4.0 125 1.0259 0.706 0.7023 0.706 0.6992 0.706 0.706 0.7058 0.7095 0.706 0.7034
1.253 4.99 156 0.9180 0.729 0.7277 0.729 0.7239 0.729 0.729 0.7291 0.7340 0.729 0.7261
1.0975 5.98 187 0.8859 0.747 0.7480 0.747 0.7437 0.747 0.747 0.7472 0.7609 0.747 0.7526
1.1122 6.98 218 0.8270 0.76 0.7606 0.76 0.7578 0.76 0.76 0.7594 0.7772 0.76 0.7727
1.0365 8.0 250 0.7806 0.775 0.7759 0.775 0.7730 0.775 0.775 0.7735 0.7957 0.775 0.7920
1.004 8.99 281 0.7472 0.796 0.7977 0.796 0.7957 0.796 0.796 0.7956 0.8193 0.796 0.8151
0.9278 9.98 312 0.7296 0.795 0.7974 0.795 0.7957 0.795 0.795 0.7953 0.8157 0.795 0.8115
0.8767 10.98 343 0.7257 0.809 0.8101 0.809 0.8078 0.809 0.809 0.8091 0.8182 0.809 0.8136
0.8656 12.0 375 0.6875 0.814 0.8137 0.8140 0.8106 0.814 0.814 0.8122 0.8207 0.814 0.8164
0.7905 12.99 406 0.7060 0.808 0.8093 0.808 0.8071 0.808 0.808 0.8068 0.8182 0.808 0.8145
0.8804 13.98 437 0.6849 0.82 0.8214 0.82 0.8183 0.82 0.82 0.8183 0.8260 0.82 0.8215
0.8265 14.98 468 0.6821 0.816 0.8171 0.816 0.8143 0.816 0.816 0.8142 0.8242 0.816 0.8206
0.7929 16.0 500 0.6877 0.818 0.8184 0.818 0.8152 0.818 0.818 0.8167 0.8240 0.818 0.8186
0.7993 16.99 531 0.6718 0.825 0.8259 0.825 0.8234 0.825 0.825 0.8227 0.8306 0.825 0.8282
0.7954 17.86 558 0.6715 0.826 0.8272 0.826 0.8242 0.826 0.826 0.8237 0.8327 0.826 0.8293

Framework versions

  • Transformers 4.28.1
  • Pytorch 2.0.0+cu118
  • Datasets 2.11.0
  • Tokenizers 0.13.3