---
license: apache-2.0
pipeline_tag: image-classification
---
# Screenshot classifier

A lightweight binary classifier for screenshot detection.

## Model

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

## Usage

```python
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`