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--- |
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license: apache-2.0 |
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base_model: google/vit-base-patch16-224-in21k |
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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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model-index: |
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- name: tsec_vit_model |
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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: 0.7689873417721519 |
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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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# tsec_vit_model |
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4821 |
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- Accuracy: 0.7690 |
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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: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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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 | |
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|:-------------:|:------:|:----:|:---------------:|:--------:| |
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| 0.4963 | 0.9873 | 39 | 0.5699 | 0.6725 | |
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| 0.4959 | 2.0 | 79 | 0.5485 | 0.7152 | |
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| 0.4879 | 2.9873 | 118 | 0.4886 | 0.7690 | |
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| 0.5243 | 4.0 | 158 | 0.5133 | 0.7468 | |
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| 0.4654 | 4.9873 | 197 | 0.4927 | 0.7516 | |
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| 0.4776 | 6.0 | 237 | 0.4901 | 0.7642 | |
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| 0.4767 | 6.9873 | 276 | 0.4652 | 0.7816 | |
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| 0.4465 | 8.0 | 316 | 0.4795 | 0.7642 | |
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| 0.467 | 8.9873 | 355 | 0.4691 | 0.7484 | |
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| 0.4121 | 9.8734 | 390 | 0.4821 | 0.7690 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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