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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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- precision |
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- recall |
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model-index: |
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- name: vit-base-patch16-224 |
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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: validation |
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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: 0.7066666666666667 |
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- name: Precision |
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type: precision |
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value: 0.5034113712374582 |
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- name: Recall |
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type: recall |
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value: 0.7066666666666667 |
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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 |
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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: 0.5891 |
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- Accuracy: 0.7067 |
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- Precision: 0.5034 |
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- Recall: 0.7067 |
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- F1 Score: 0.5880 |
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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: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 256 |
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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: 15 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:| |
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| No log | 1.0 | 4 | 0.5970 | 0.725 | 0.5256 | 0.725 | 0.6094 | |
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| No log | 2.0 | 8 | 0.5990 | 0.7292 | 0.8028 | 0.7292 | 0.6191 | |
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| No log | 3.0 | 12 | 0.5648 | 0.725 | 0.5256 | 0.725 | 0.6094 | |
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| 0.6217 | 4.0 | 16 | 0.6035 | 0.7042 | 0.6625 | 0.7042 | 0.6709 | |
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| 0.6217 | 5.0 | 20 | 0.5560 | 0.7333 | 0.8050 | 0.7333 | 0.6286 | |
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| 0.6217 | 6.0 | 24 | 0.5656 | 0.7167 | 0.6184 | 0.7167 | 0.6194 | |
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| 0.6217 | 7.0 | 28 | 0.5552 | 0.7292 | 0.8028 | 0.7292 | 0.6191 | |
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| 0.5729 | 8.0 | 32 | 0.5532 | 0.7292 | 0.7126 | 0.7292 | 0.6263 | |
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| 0.5729 | 9.0 | 36 | 0.5634 | 0.7292 | 0.6863 | 0.7292 | 0.6453 | |
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| 0.5729 | 10.0 | 40 | 0.5589 | 0.7333 | 0.7009 | 0.7333 | 0.6536 | |
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| 0.5729 | 11.0 | 44 | 0.5676 | 0.7292 | 0.6848 | 0.7292 | 0.6612 | |
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| 0.5599 | 12.0 | 48 | 0.5655 | 0.7333 | 0.6952 | 0.7333 | 0.6688 | |
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| 0.5599 | 13.0 | 52 | 0.5692 | 0.7333 | 0.6954 | 0.7333 | 0.6816 | |
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| 0.5599 | 14.0 | 56 | 0.5746 | 0.725 | 0.6864 | 0.725 | 0.6863 | |
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| 0.5382 | 15.0 | 60 | 0.5752 | 0.7208 | 0.6832 | 0.7208 | 0.6864 | |
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### Framework versions |
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- Transformers 4.33.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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