feat(private-detector): [0.1.0] Move repo to public github

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README.md

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+# 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
+
+## Model
+
+The SavedModel can be found in `saved_model/`, ensure you have [Git LFS](https://git-lfs.github.com/) installed to pull the full files.
+
+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](pending)
+
+## Inference
+
+Inference is pretty simple and an example has been given in `inference.py`
+
+```sh
+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>
+
+## 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/`
+
+Set up a JSON file with links to your image path lists for each class:
+
+```json
+{
+    "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
+
+```txt
+/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:
+
+```sh
+conda env create -f environment.yaml
+conda activate private_detector
+```
+
+And then retrain like so:
+
+```sh
+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`|

environment.yaml

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+name: private_detector
+channels:
+  - conda-forge
+  - nvidia
+  - anaconda
+dependencies:
+  - python=3.8.*
+  - cudnn=8.0.*
+  - cudatoolkit=11.0.*
+  - pip=20.2.*
+  - pip:
+    - tensorflow-gpu==2.4.*
+    - pandas==1.1.5
+    - openpyxl==3.0.7
+    - tensorflow_addons==0.13.0
+    - protobuf==3.20.1
+    - pyyaml==6.0