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--- |
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tags: |
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- generated_from_trainer |
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datasets: |
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- imagefolder |
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metrics: |
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- accuracy |
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- f1 |
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- recall |
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- precision |
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model-index: |
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- name: dit-base-Business_Documents_Classified_v2 |
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results: |
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- task: |
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name: Image Classification |
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type: image-classification |
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dataset: |
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name: imagefolder |
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type: imagefolder |
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config: data |
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split: train |
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args: data |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.826 |
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language: |
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- en |
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pipeline_tag: image-classification |
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--- |
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# dit-base-Business_Documents_Classified_v2 |
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This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6715 |
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- Accuracy: 0.826 |
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- Weighted f1: 0.8272 |
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- Micro f1: 0.826 |
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- Macro f1: 0.8242 |
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- Weighted recall: 0.826 |
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- Micro recall: 0.826 |
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- Macro recall: 0.8237 |
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- Weighted precision: 0.8327 |
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- Micro precision: 0.826 |
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- Macro precision: 0.8293 |
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## Model description |
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This is a classification model of 16 different types of documents. |
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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 |
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## Intended uses & limitations |
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This model is intended to demonstrate my ability to solve a complex problem using technology. |
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## Training and evaluation data |
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Dataset Source: https://www.kaggle.com/datasets/shaz13/real-world-documents-collections |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 18 |
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### Training results |
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| 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 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:| |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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### Framework versions |
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- Transformers 4.28.1 |
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- Pytorch 2.0.0+cu118 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.3 |
