Awesome

Private Detector

This is the repo for Bumble's Private Detector™ model - an image classifier that can detect lewd images.

The internal repo has been heavily refactored and released as a fully open-source project to allow for the wider community to use and finetune a Private Detector model of their own. You can download the pretrained SavedModel, Frozen Model and checkpoint here

Model

The SavedModel can be found in saved_model/ within private_detector.zip above

The model is based on Efficientnet-v2 and trained on our internal dataset of lewd images - more information can be found at the whitepaper here or here

Inference

Inference is pretty simple and an example has been given in inference.py. The model is released as a SavedModel so it can be deployed in many different ways, but here's a quick runthrough of one way to get it working for those less familiar with Python/Tensorflow.

First you need to install Python and Conda on your system and go to the Terminal/Command Prompt on your machine

Then you can use the environment.yaml file to install the necessary packages to run the inference.

conda env create -f environment.yaml
conda activate private_detector

Once that's set up, you can run the inference script. Simply replace the sample .jpg file paths below with your own

python3 inference.py \
    --model saved_model/ \
    --image_paths \
        Yes_samples/1.jpg \
        Yes_samples/2.jpg \
        Yes_samples/3.jpg \
        Yes_samples/4.jpg \
        Yes_samples/5.jpg \
        No_samples/1.jpg \
        No_samples/2.jpg \
        No_samples/3.jpg \
        No_samples/4.jpg \
        No_samples/5.jpg \

<details> <summary>Sample Output</summary> <code>

Probability: 93.71% - Yes_samples/1.jpg
Probability: 93.43% - Yes_samples/2.jpg
Probability: 94.06% - Yes_samples/3.jpg
Probability: 94.08% - Yes_samples/4.jpg
Probability: 91.01% - Yes_samples/5.jpg
Probability: 9.76% - No_samples/1.jpg
Probability: 7.14% - No_samples/2.jpg
Probability: 8.83% - No_samples/3.jpg
Probability: 4.87% - No_samples/4.jpg
Probability: 5.29% - No_samples/5.jpg

</code> </details>

Serving

See Tensorflow Serving example

Additional Training

You can finetune the model yourself on your own data, to do so is fairly simple - though you will need the checkpoint files as can be found in saved_checkpoint/ in private_detector.zip

Set up a JSON file with links to your image path lists for each class:

{
    "Yes": {
        "path": "/home/sofarrell/private_detector/Yes.txt",
        "label": 0
    },
    "No": {
         "path": "/home/sofarrell/private_detector/No.txt",
         "label": 1
    }
}

With each .txt file listing off the image paths to your images

/home/sofarrell/private_detector_images/Yes/1093840880_309463828.jpg
/home/sofarrell/private_detector_images/Yes/657954182_3459624.jpg
/home/sofarrell/private_detector_images/Yes/1503714421_3048734.jpg

You can create the training environment with conda:

conda env create -f environment.yaml
conda activate private_detector

And then retrain like so:

python3 ./train.py \
    --train_json /home/sofarrell/private_detector/train_classes.json \
    --eval_json /home/sofarrell/private_detector/eval_classes.json \
    --checkpoint_dir saved_checkpoint/ \
    --train_id retrained_private_detector

The training script has several parameters that can be tweaked:

Command	Description	Type	Default
`train_id`	ID for this particular training run	str
`train_json`	JSON file(s) which describes classes and contains lists of filenames of data files	List[str]
`eval_json`	Validation json file which describes classes and contains lists of filenames of data files	str
`num_epochs`	Number of epochs to train for	int
`batch_size`	Number of images to process in a batch	int	`64`
`checkpoint_dir`	Directory to store checkpoints in	str
`model_dir`	Directory to store graph in	str	`.`
`data_format`	Data format: [channels_first, channels_last]	str	`channels_last`
`initial_learning_rate`	Initial learning rate	float	`1e-4`
`min_learning_rate`	Minimal learning rate	float	`1e-6`
`min_eval_metric`	Minimal evaluation metric to start saving models	float	`0.01`
`float_dtype`	Float Dtype to use in image tensors: [16, 32]	int	`16`
`steps_per_train_epoch`	Number of steps per train epoch	int	`800`
`steps_per_eval_epoch`	Number of steps per evaluation epoch	int	`1`
`reset_on_lr_update`	Whether to reset to the best model after learning rate update	bool	`False`
`rotation_augmentation`	Rotation augmentation angle, value <= 0 disables it	float	`0`
`use_augmentation`	Add speckle, v0, random or color distortion augmentation	str
`scale_crop_augmentation`	Resize image to the model's size times this scale and then randomly crop needed size	float	`1.4`
`reg_loss_weight`	L2 regularization weight	float	`0`
`skip_saving_epochs`	Do not save good checkpoint and update best metric for this number of the first epochs	int	`0`
`sequential`	Use sequential run over randomly shuffled filenames vs equal sampling from each class	bool	`False`
`eval_threshold`	Threshold above which to consider a prediction positive for evaluation	float	`0.5`
`epochs_lr_update`	Maximum number of epochs without improvement used to reset/decrease learning rate	int	`20`