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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: nsfw-image-detector |
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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: 0.9315615772103526 |
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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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# nsfw-image-detector |
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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: 0.8138 |
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- Accuracy: 0.9316 |
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- Accuracy K: 0.9887 |
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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: 2e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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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_steps: 500 |
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- num_epochs: 10 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Accuracy K | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:| |
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| 0.7836 | 1.0 | 720 | 0.3188 | 0.9085 | 0.9891 | |
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| 0.2441 | 2.0 | 1440 | 0.2382 | 0.9257 | 0.9936 | |
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| 0.1412 | 3.0 | 2160 | 0.2334 | 0.9335 | 0.9932 | |
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| 0.0857 | 4.0 | 2880 | 0.2934 | 0.9347 | 0.9934 | |
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| 0.0569 | 5.0 | 3600 | 0.4500 | 0.9307 | 0.9927 | |
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| 0.0371 | 6.0 | 4320 | 0.5524 | 0.9357 | 0.9910 | |
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| 0.0232 | 7.0 | 5040 | 0.6691 | 0.9347 | 0.9913 | |
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| 0.02 | 8.0 | 5760 | 0.7408 | 0.9335 | 0.9917 | |
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| 0.0154 | 9.0 | 6480 | 0.8138 | 0.9316 | 0.9887 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.0.0 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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