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--- |
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license: apache-2.0 |
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base_model: google/vit-base-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-base-patch16-224-finetuned-ind-17-imbalanced-aadhaarmask-new-parameter |
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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: 0.8339719029374202 |
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- name: Recall |
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type: recall |
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value: 0.8339719029374202 |
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- name: F1 |
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type: f1 |
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value: 0.8319571049551264 |
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- name: Precision |
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type: precision |
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value: 0.8325133593723552 |
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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-base-patch16-224-finetuned-ind-17-imbalanced-aadhaarmask-new-parameter |
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This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3507 |
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- Accuracy: 0.8340 |
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- Recall: 0.8340 |
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- F1: 0.8320 |
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- Precision: 0.8325 |
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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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| No log | 0.9974 | 293 | 0.6168 | 0.7923 | 0.7923 | 0.7737 | 0.7684 | |
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| No log | 1.9983 | 587 | 0.4599 | 0.8110 | 0.8110 | 0.8056 | 0.8085 | |
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| No log | 2.9991 | 881 | 0.4305 | 0.8233 | 0.8233 | 0.8211 | 0.8250 | |
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| No log | 4.0 | 1175 | 0.3966 | 0.8365 | 0.8365 | 0.8323 | 0.8452 | |
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| No log | 4.9974 | 1468 | 0.4100 | 0.8221 | 0.8221 | 0.8195 | 0.8219 | |
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| No log | 5.9983 | 1762 | 0.3890 | 0.8412 | 0.8412 | 0.8375 | 0.8466 | |
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| No log | 6.9991 | 2056 | 0.3659 | 0.8357 | 0.8357 | 0.8335 | 0.8386 | |
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| No log | 8.0 | 2350 | 0.3562 | 0.8395 | 0.8395 | 0.8379 | 0.8403 | |
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| No log | 8.9974 | 2643 | 0.3613 | 0.8382 | 0.8382 | 0.8373 | 0.8391 | |
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| 0.4339 | 9.9745 | 2930 | 0.3405 | 0.8455 | 0.8455 | 0.8447 | 0.8467 | |
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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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