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---
license: apache-2.0
metrics:
- accuracy
pipeline_tag: image-classification
tags:
- MobileNetV2
- accident-detection
library_name: transformers
---
An image classification model for detecting car crashes from traffic cams. An easier to run version of Crashly is currently in development. To run this model, use the following code snippet.
```
import numpy as np
from PIL import Image
import tensorflow as tf
# Load TFLite model and allocate tensors.
interpreter = tf.lite.Interpreter(model_path="{model_name}.tflite")
interpreter.allocate_tensors()
# Get input and output tensors.
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
input_shape = input_details[0]['shape']
# Load and preprocess image
def load_image(image_path):
img = Image.open(image_path).convert('RGB')
img = img.resize([input_shape[1], input_shape[2]])
img = np.asarray(img, dtype='float32') / 255
# Return a scaled array between -1 and 1
return img * 2 - 1
if __name__ == "__main__":
input_data = load_image("/tmp/your-image-here.jpg")
interpreter.set_tensor(input_details[0]['index'], input_data)
interpreter.invoke()
# The function `get_tensor()` returns a copy of the tensor data.
# Use `tensor()` in order to get a pointer to the tensor.
output_data = interpreter.get_tensor(output_details[0]['index'])
print(output_data)
``` |