YOLOv11 TFLite Model Not Working Correctly in TensorFlow: Debugging ALPR Inference Issues

Training an Automatic License Plate Recognition (ALPR) detection model using YOLOv11 and TensorFlow Lite (TFLite) can significantly streamline license plate detection tasks. Recently, I trained a custom YOLOv11 model leveraging the robust Roboflow ALPR dataset. After training, I successfully converted the model from YOLOv11 format into TFLite using the intuitive Ultralytics API.

However, after implementing inference using TensorFlow directly with my TFLite model, I unexpectedly encountered issues—the inference results weren’t matching the successful predictions I’d seen using the standard Ultralytics YOLO inference pipeline. In this article, I’ll explore the differences, investigate the possible causes, and provide resources to debug and fix TFLite inference issues effectively.

Successful Detection Using Ultralytics YOLO

First, let’s consider the scenario where detection results were working as expected. Using Ultralytics’ official YOLO Python code, inference with my YOLOv11 model performed well on detecting license plates, even handling Persian numeric characters seamlessly.

Here’s the original Python inference snippet that yielded accurate results:


from ultralytics import YOLO
import cv2

# Load YOLOv11 trained model
model = YOLO('yolov11_alpr.pt')

# Run inference
results = model.predict(source='image.jpg')

# Visualize results
annotated_frame = results[0].plot()
cv2.imshow("ALPR Detection", annotated_frame)
cv2.waitKey(0)

The inference generated impressive graphical annotations highlighting detected license plates. When analyzed in detail, the model correctly recognized Persian numbers—proving the model trained well and inference within Ultralytics was robust.

Here’s a screenshot showcasing these successful detections (example only):

Successful ALPR Detection Example by YOLOv11

The Problem with Direct TensorFlow Inference

Despite the model performing admirably within the Ultralytics environment, direct inference using TensorFlow with the TFLite model resulted in missing or incorrect detections.

Below is my approach to testing the converted TFLite model:


import tensorflow as tf
import numpy as np
import cv2

# Load TFLite model
interpreter = tf.lite.Interpreter(model_path="yolov11_alpr.tflite")
interpreter.allocate_tensors()

input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

# Preprocess the image
image = cv2.imread("image.jpg")
resized_image = cv2.resize(image, (640, 640))
input_image = resized_image.astype(np.float32) / 255.0  # normalizing if required
input_tensor = np.expand_dims(input_image, axis=0)

# Set input tensor and run inference
interpreter.set_tensor(input_details[0]['index'], input_tensor)
interpreter.invoke()

# Fetch output tensors
output_data = interpreter.get_tensor(output_details[0]['index'])
print(output_data)

Unfortunately, instead of meaningful bounding boxes or scores, I often received tensors with strange or empty values. This discrepancy made clear that something critical was missing in this inference strategy.

Let’s dive deeper into the issue:

Incorrect Detection or No Detection at All: Direct TensorFlow inference didn’t yield the correct bounding box predictions compared to Ultralytics inference.
Output Tensor Issue: Possibly, the outputs aren’t directly interpretable bounding boxes, or extra preprocessing and decoding are required.
No Automatic Visualization: Unlike Ultralytics’ built-in visualization, manual decoding of model outputs might be necessary for valid visualization.

Why is there Such a Big Difference?

The main questions arising at this point are:

What is causing the disparity between Ultralytics YOLO inference and direct TensorFlow inference using TFLite?
Are there critical preprocessing or post-processing steps I overlooked in the direct inference pipeline?
How can we correctly decode bounding box coordinates, confidence scores, and class predictions from raw TFLite outputs?

Usually, frameworks like Ultralytics YOLO library automatically handle preprocessing (normalization, resizing) and important post-processing methods like Non-Max Suppression, which isn’t automatically carried out by TensorFlow inference pipelines.

Identifying Missing Steps

Here are a few critical points that might be missing from your TensorFlow inference loop:

Non-Maximum Suppression (NMS): YOLO models rely heavily on NMS for filtering and picking the best bounding boxes from a plethora of raw detections.
Confidence Thresholding: YOLO outputs many boxes initially, and you must filter low-confidence detections manually.
Coordinate Conversion: YOLO output coordinates usually need decoding (from grid-based local predictions to full-image predictions).

You can find more details on NMS and bounding box decoding on TensorFlow’s official documentation or this informative Stack Overflow thread discussing NMS usage.

Testing the TFLite Model for Yourself

I’ve made the TFLite model publicly available to facilitate community debugging and collaboration. Please download and experiment with it yourself:

Download YOLOv11 ALPR TFLite Model

I encourage you to try running TensorFlow inference on your local datasets or applications, adjusting preprocessing or post-processing steps, and share your findings via comments or community discussions.

Community Help Request and Collaboration

The issues described above are common when migrating YOLO models from libraries like Ultralytics to TFLite inference using TensorFlow. If you’ve encountered similar situations previously, your expertise can make a significant impact.

Specifically, your feedback on the following points would greatly help:

Detailed insights on effective YOLO post-processing steps in native TensorFlow/TFLite inference.
Example TensorFlow or TFLite inference snippets that provide correct detection results.
Tips on resolving disparities between Ultralytics scripts and direct TensorFlow inference pipelines.

Feel free to contribute your changes, suggestions, or code examples on our public GitHub repository.

I’ve also provided other useful resources here:

Finally, remember to check out our dedicated Python category page on Python tutorials and debugging tips for more useful insights.

Making YOLOv11 ALPR detection smooth and easy-to-use across different frameworks is achievable through community input. Have you faced similar YOLO inference issues? Please share your insights and solutions in the comments below—let’s tackle this challenge together!