The .tflite file is our model. You can use a program like Netron to view the neural network. The program captures a frame from the camera using OpenCV, resizes the frame to 300x300 pixels # Perform the actual detection by running the model with the image as input interpreter.set_tensor...
Weight Quantization - Input/Output=float32 converter = tf.lite.TFLiteConverter.from_saved_model 1. Convert the data acquired by Tensorflow Datasets to Numpy 2. Resize the image size to INPUT size 720x720 3. Normalize image data to the range from -1 to 1 4. To match the shape of the INPUT [1...
Load TFLite model. Resize input and output tensors shapes. Compare prediction results. Visualize predictions from TFLite models. Resize input and output tensors shapes. Input shape of loaded TFLite model is 1x224x224x3, what means that we can make predictions for single image.
Tensorflow Lite是针对移动设备和嵌入式设备的轻量化解决方案,占用空间小,低延迟。Tensorflow Lite在android8.1以上的设备上可以通过ANNA启用硬件加速。