https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/examples/python/
TensorFlow Lite Python image classification demo# Get photo curl https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/lite/examples/label_image/testdata/grace_hopper.bmp > /tmp/grace_hopper.bmp # Get model curl https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224.tgz | tar xzv -C /tmp # Get labels curl https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_1.0_224_frozen.tgz | tar xzv -C /tmp mobilenet_v1_1.0_224/labels.txt mv /tmp/mobilenet_v1_1.0_224/labels.txt /tmp/ 首先，我们需要把该下载的图片、模型、标签，下载到本地 然后运行下面的指令: python3 label_image.py --model_file mobilenet_v1_1.0_224/mobilenet_v1_1.0_224.tflite --label_file mobilenet_v1_1.0_224_frozen/mobilenet_v1_1.0_224/labels.txt --image grace_hopper.bmp 输出的预测结果如下所示: 0.919720: 653:military uniform 0.017762: 907:Windsor tie 0.007507: 668:mortarboard 0.005419: 466:bulletproof vest 0.003828: 458:bow tie, bow-tie, bowtie time: 9.754ms
此处我们需要先调用模型 1000 次，当然实际的时候不需要这么多次，这里只是为了把加载模型的时间排除在外
更改了一部分代码
import tensorflow as tf 变成 import tflite_runtime.interpreter as tflite interpreter = tf.lite.Interpreter(model_path=args.model_file) 变成 interpreter = tflite.Interpreter(model_path=args.model_file) 0.919721: 653:military uniform 0.017762: 907:Windsor tie 0.007507: 668:mortarboard 0.005419: 466:bulletproof vest 0.003828: 458:bow tie, bow-tie, bowtie time: 14.443ms 对比上面的 9.754ms 来说，好像变慢了，原因是什么？ 而且更加的耗费 CPU 资源了，原因是？
