Update README.md

README.md

@@ -6,4 +6,40 @@ pipeline_tag: image-classification
 tags:
 - MobileNetV2
 - accident-detection
----
+---
+
+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)
+`