metadata

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

nsfw-image-detector

This model is a fine-tuned version of 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