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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: attraction-classifier |
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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: 0.8021680216802168 |
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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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# attraction-classifier |
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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: 0.5632 |
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- Accuracy: 0.8022 |
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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: 69 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.05 |
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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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| 0.6613 | 0.48 | 100 | 0.6067 | 0.6477 | |
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| 0.6115 | 0.97 | 200 | 0.5579 | 0.6992 | |
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| 0.5542 | 1.45 | 300 | 0.5501 | 0.7182 | |
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| 0.4758 | 1.93 | 400 | 0.5108 | 0.7534 | |
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| 0.5219 | 2.42 | 500 | 0.5122 | 0.7561 | |
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| 0.4631 | 2.9 | 600 | 0.4842 | 0.7832 | |
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| 0.3866 | 3.38 | 700 | 0.5298 | 0.7480 | |
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| 0.3704 | 3.86 | 800 | 0.4963 | 0.7453 | |
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| 0.4222 | 4.35 | 900 | 0.4832 | 0.7561 | |
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| 0.3162 | 4.83 | 1000 | 0.4807 | 0.7778 | |
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| 0.2686 | 5.31 | 1100 | 0.4949 | 0.7859 | |
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| 0.304 | 5.8 | 1200 | 0.4719 | 0.7751 | |
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| 0.2246 | 6.28 | 1300 | 0.5014 | 0.8157 | |
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| 0.2503 | 6.76 | 1400 | 0.5077 | 0.8103 | |
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| 0.169 | 7.25 | 1500 | 0.4630 | 0.8238 | |
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| 0.2248 | 7.73 | 1600 | 0.5329 | 0.7832 | |
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| 0.164 | 8.21 | 1700 | 0.5608 | 0.7859 | |
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| 0.208 | 8.7 | 1800 | 0.5632 | 0.8022 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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