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--- |
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license: apache-2.0 |
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base_model: google/vit-large-patch16-224 |
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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: vit-large-patch16-224-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: 0.8420604512558536 |
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- name: Recall |
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type: recall |
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value: 0.8420604512558536 |
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- name: F1 |
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type: f1 |
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value: 0.840458775689156 |
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- name: Precision |
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type: precision |
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value: 0.8450034699086092 |
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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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# vit-large-patch16-224-finetuned-ind-17-imbalanced-aadhaarmask |
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This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co./google/vit-large-patch16-224) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3294 |
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- Accuracy: 0.8421 |
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- Recall: 0.8421 |
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- F1: 0.8405 |
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- Precision: 0.8450 |
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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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| 0.5269 | 0.9974 | 293 | 0.5393 | 0.8029 | 0.8029 | 0.7943 | 0.7941 | |
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| 0.4275 | 1.9983 | 587 | 0.4630 | 0.8182 | 0.8182 | 0.8103 | 0.8255 | |
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| 0.4681 | 2.9991 | 881 | 0.4346 | 0.8408 | 0.8408 | 0.8358 | 0.8557 | |
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| 0.3721 | 4.0 | 1175 | 0.3631 | 0.8450 | 0.8450 | 0.8417 | 0.8541 | |
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| 0.4054 | 4.9974 | 1468 | 0.3536 | 0.8455 | 0.8455 | 0.8445 | 0.8491 | |
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| 0.2519 | 5.9983 | 1762 | 0.3747 | 0.8421 | 0.8421 | 0.8391 | 0.8549 | |
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| 0.2923 | 6.9991 | 2056 | 0.3664 | 0.8395 | 0.8395 | 0.8402 | 0.8467 | |
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| 0.2288 | 8.0 | 2350 | 0.3496 | 0.8382 | 0.8382 | 0.8377 | 0.8442 | |
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| 0.1642 | 8.9974 | 2643 | 0.3455 | 0.8463 | 0.8463 | 0.8444 | 0.8468 | |
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| 0.1783 | 9.9745 | 2930 | 0.3468 | 0.8476 | 0.8476 | 0.8463 | 0.8490 | |
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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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