| | --- |
| | license: apache-2.0 |
| | base_model: google/vit-base-patch16-224-in21k |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - imagefolder |
| | metrics: |
| | - accuracy |
| | model-index: |
| | - name: nsfw-image-detector |
| | results: |
| | - task: |
| | name: Image Classification |
| | type: image-classification |
| | dataset: |
| | name: imagefolder |
| | type: imagefolder |
| | config: default |
| | split: train |
| | args: default |
| | metrics: |
| | - name: Accuracy |
| | type: accuracy |
| | value: 0.9315615772103526 |
| | --- |
| | |
| | <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | # nsfw-image-detector |
| |
|
| | 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. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.8138 |
| | - Accuracy: 0.9316 |
| | - Accuracy K: 0.9887 |
| |
|
| | You can access 384 version on: |
| |
|
| | https://huggingface.co/LukeJacob2023/nsfw-image-detector-384 |
| |
|
| | ## Model description |
| |
|
| | Labels: ['drawings', 'hentai', 'neutral', 'porn', 'sexy'] |
| |
|
| | ## Intended uses & limitations |
| |
|
| | ## Training and evaluation data |
| |
|
| | A custom dataset about 28k images, if you need to improve your domain's accurate, you can contribute the dataset to me. |
| |
|
| | ## Training procedure |
| |
|
| | ### Training hyperparameters |
| |
|
| | The following hyperparameters were used during training: |
| | - learning_rate: 2e-05 |
| | - train_batch_size: 32 |
| | - eval_batch_size: 32 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - lr_scheduler_warmup_steps: 500 |
| | - num_epochs: 10 |
| | - mixed_precision_training: Native AMP |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | Accuracy K | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:| |
| | | 0.7836 | 1.0 | 720 | 0.3188 | 0.9085 | 0.9891 | |
| | | 0.2441 | 2.0 | 1440 | 0.2382 | 0.9257 | 0.9936 | |
| | | 0.1412 | 3.0 | 2160 | 0.2334 | 0.9335 | 0.9932 | |
| | | 0.0857 | 4.0 | 2880 | 0.2934 | 0.9347 | 0.9934 | |
| | | 0.0569 | 5.0 | 3600 | 0.4500 | 0.9307 | 0.9927 | |
| | | 0.0371 | 6.0 | 4320 | 0.5524 | 0.9357 | 0.9910 | |
| | | 0.0232 | 7.0 | 5040 | 0.6691 | 0.9347 | 0.9913 | |
| | | 0.02 | 8.0 | 5760 | 0.7408 | 0.9335 | 0.9917 | |
| | | 0.0154 | 9.0 | 6480 | 0.8138 | 0.9316 | 0.9887 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.36.2 |
| | - Pytorch 2.0.0 |
| | - Datasets 2.15.0 |
| | - Tokenizers 0.15.0 |
| | |
