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--- |
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license: apache-2.0 |
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base_model: microsoft/beit-base-patch16-224-pt22k-ft22k |
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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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- recall |
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- f1 |
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- precision |
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model-index: |
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- name: beit-base-patch16-224-pt22k-ft22k-finetuned-ind-17-imbalanced-aadhaarmask |
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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: default |
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split: train |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.8450404427415922 |
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- name: Recall |
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type: recall |
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value: 0.8450404427415922 |
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- name: F1 |
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type: f1 |
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value: 0.8442233792705293 |
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- name: Precision |
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type: precision |
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value: 0.8494143266059094 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# beit-base-patch16-224-pt22k-ft22k-finetuned-ind-17-imbalanced-aadhaarmask |
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This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3480 |
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- Accuracy: 0.8450 |
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- Recall: 0.8450 |
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- F1: 0.8442 |
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- Precision: 0.8494 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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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: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | F1 | Precision | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:------:|:---------:| |
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| 0.5859 | 0.9974 | 293 | 0.6117 | 0.8114 | 0.8114 | 0.7891 | 0.8139 | |
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| 0.5281 | 1.9983 | 587 | 0.4362 | 0.8442 | 0.8442 | 0.8375 | 0.8484 | |
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| 0.4214 | 2.9991 | 881 | 0.4228 | 0.8438 | 0.8438 | 0.8392 | 0.8529 | |
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| 0.4221 | 4.0 | 1175 | 0.4121 | 0.8382 | 0.8382 | 0.8331 | 0.8495 | |
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| 0.4127 | 4.9974 | 1468 | 0.3692 | 0.8476 | 0.8476 | 0.8454 | 0.8511 | |
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| 0.3122 | 5.9983 | 1762 | 0.3741 | 0.8408 | 0.8408 | 0.8394 | 0.8462 | |
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| 0.3079 | 6.9991 | 2056 | 0.3628 | 0.8429 | 0.8429 | 0.8403 | 0.8445 | |
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| 0.2851 | 8.0 | 2350 | 0.3635 | 0.8412 | 0.8412 | 0.8389 | 0.8412 | |
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| 0.297 | 8.9974 | 2643 | 0.3407 | 0.8510 | 0.8510 | 0.8497 | 0.8545 | |
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| 0.2109 | 9.9745 | 2930 | 0.3566 | 0.8421 | 0.8421 | 0.8406 | 0.8418 | |
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### Framework versions |
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- Transformers 4.40.1 |
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- Pytorch 2.2.0a0+81ea7a4 |
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- Datasets 2.19.0 |
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- Tokenizers 0.19.1 |
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