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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: 0.8044444444444444 |
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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: 0.5059 |
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- Accuracy: 0.8044 |
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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: linear |
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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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| 0.5884 | 0.59 | 150 | 0.5623 | 0.7022 | |
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| 0.4854 | 1.19 | 300 | 0.5428 | 0.7422 | |
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| 0.5224 | 1.78 | 450 | 0.5069 | 0.7444 | |
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| 0.4026 | 2.37 | 600 | 0.5105 | 0.7556 | |
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| 0.4381 | 2.96 | 750 | 0.4564 | 0.7844 | |
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| 0.3707 | 3.56 | 900 | 0.4668 | 0.7844 | |
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| 0.3649 | 4.15 | 1050 | 0.4684 | 0.7911 | |
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| 0.3686 | 4.74 | 1200 | 0.4625 | 0.7867 | |
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| 0.2984 | 5.34 | 1350 | 0.4404 | 0.8289 | |
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| 0.3545 | 5.93 | 1500 | 0.4282 | 0.8 | |
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| 0.2921 | 6.52 | 1650 | 0.5068 | 0.7956 | |
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| 0.2052 | 7.11 | 1800 | 0.5059 | 0.8044 | |
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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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