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README.md CHANGED
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  ---
 
 
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  tags:
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- - image-classification
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- - pytorch
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- - huggingpics
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  metrics:
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  - accuracy
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  model-index:
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- - name: nsfw-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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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.9200000166893005
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- datasets:
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- - deepghs/nsfw_detect
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  ---
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- # nsfw-classifier
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- NSFW Classifier using [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k)
 
 
 
 
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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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+
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+ # nsfw-image-detector
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+
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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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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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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+
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+ ### Training results
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+
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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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+
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+
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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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