Screenshot classifier

A lightweight binary classifier for screenshot detection.

Model

mobilenetv3_small_screenshot.keras — trained Keras model for screenshot / not_screenshot classification

Usage

import tensorflow as tf
from tensorflow import keras

model = keras.models.load_model("mobilenetv3_small_screenshot.keras")

# Example inference
img = keras.utils.load_img("image.png", target_size=(224, 224))
arr = keras.utils.img_to_array(img)
arr = tf.keras.applications.mobilenet_v3.preprocess_input(arr)
arr = arr[None, ...]
pred = model.predict(arr)
score = float(pred[0, 0])
label = "screenshot" if score >= 0.5 else "not_screenshot"
print(label, score)

Input format

RGB images
resized to 224x224
model expects a single sigmoid score

Prediction threshold

score >= 0.5 → screenshot
score < 0.5 → not_screenshot

Notes

Model backbone: MobileNetV3Small pretrained on ImageNet
Output activation: sigmoid
Loss used during training: binary_crossentropy

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