converter = tf.lite.TFLiteConverter.from_keras_model( ‘./signet.h5’) tflite_model = converter.convert() with open(‘model.tflite’, ‘wb’) as f: f.write(tflite_model) AttributeError Traceback (most recent call last) ~AppDataLocalTemp/ipykernel_2464/3288577076.py in <module> 1 converter = tf.lite.TFLiteConverter.from_keras_model( ‘./signet.h5’) —-> 2 tflite_model = converter.convert() 3 # Save the model. 4 with open(‘model.tflite’, ‘wb’) as f: 5 f.write(tflite_model) ~anaconda3envstensorflowlibsite-packagestensorflowlitepythonlite.py in convert(self) 795 # to None. 796 # Once ..
I am trying to use a tensorflow.lite.interpreter file to inferencing some audio stream. During with I have run into the following error. Here is the input array which I try to pass to the tensorflow.lite.interpreter: 2021-10-22 19:35:36 frame type: <class ‘numpy.ndarray’> 2021-10-22 19:35:36 frame data type: float32 2021-10-22 19:35:36 frame size:44032 2021-10-22 19:50:17 frame data: ..
I have created a custom tflite model for detecting objects (LEGO bricks) using google teachablemachine to be used for an image classification sorting application. Now I want to use this model in the provided code example from Tensorflow: https://github.com/tensorflow/examples/blob/master/lite/examples/image_classification/raspberry_pi/classify_picamera.py Unfortunately, I got the following issue and I have no idea how to fix it. Traceback ..
I’m attempting to train the tflite_model_maker recommendation on custom data but I’m running into issues with turning my CSV into a dataset that it’ll accept. All of the examples I’ve found online are utilizing the MovieLens dataset which skips over this problem, and the steps I’ve found for just tflite don’t seem to work here. ..
I have trained ssd model using pre-trained SSD model from Google and converted it to tflite. I trained it for 10 classes and convert it to tflite. Below is the code that I used to call converted tflite model to inspect the results import tensorflow as tf MODEL_PATH = ‘tflite_model_path’ IMAGE_PATH = ‘image of .jpgeg ..
I am training a Model in Tensorflow with a variable batchsize (Input: [None, 320, 240, 3]). The problem is during post-training quantization I can not have any dynamic input, thus no "None" and with edgetpu compiler I can not have batchsizes greater than 1. My current approach is to train one more epoch with a ..
I have a tflite model which accepts float32 input. Works fine if I load model each time on prediction. But for faster processing, I want to do as below. Then it’s throwing an error of – RuntimeError: There is at least 1 reference to internal data in the interpreter in the form of a NumPy ..
I have quantized model with float32. After making tflite model it’s predicting perfectly with single image but when using in while loop it’s showing an error. I tried to follow the instruction of TensorFlow here but didn’t understand their way. CODE: def generate_frames(frame): while True: image = cv2.resize(frame,(256,256)) #converting into float32 image = tf.image.convert_image_dtype((image/255.0), dtype=tf.float32).numpy() ..
Preciously I have set my EfficientDetLite4 model "grad_checkpoint=true" in config.yaml. And it had successfully generated some checkpoints. However, I can’t figure out how to use these checkpoints when I want to continue training based on them. Every time I train the model it just start from the beginning, not from my checkpoints. The following picture ..
I have trained the fall and not-fall person detection model using the tflite model maker, and I have tested it while training, but I want to test by loading the tflite file and just giving one image. Source: Python..