我训练了5种训练集，加上颜色识别 传输到单片机上，如何提高运行速度呢？

thresholds = [
(15, 76, 9, 68, -40, 47), #
(30, 100, -64, -8, -32, 32), #
(25, 77, -24, 10, -40, 2), #
(0, 15, 0, 40, -80, -20)] #
A_net = "A_trained.tflite"
A_labels = [line.rstrip('\n') for line in open("a_labels.txt")]
B_net = "B_trained.tflite"
B_labels = [line.rstrip('\n') for line in open("b_labels.txt")]
C_net = "C_trained.tflite"
C_labels = [line.rstrip('\n') for line in open("c_labels.txt")]
D_net = "D_trained.tflite"
D_labels = [line.rstrip('\n') for line in open("d_labels.txt")]
E_net = "E_trained.tflite"
E_labels = [line.rstrip('\n') for line in open("e_labels.txt")]

@kidswong999 可不可以使分类出来的物体确定出现在画面中心呢，这样就可以不用框出来了，我们要的就是确定识别分类出来的物体的位置，(坐标)

# Edge Impulse - OpenMV Image Classification Example

import sensor, image, time, os, tf, pyb

sensor.reset()                         # Reset and initialize the sensor.
sensor.set_pixformat(sensor.RGB565)    # Set pixel format to RGB565 (or GRAYSCALE)
sensor.set_framesize(sensor.QVGA)      # Set frame size to QVGA (320x240)
sensor.set_windowing((240, 240))       # Set 240x240 window.
sensor.skip_frames(time=2000)          # Let the camera adjust.

net = "trained.tflite"
labels = [line.rstrip('\n') for line in open("labels.txt")]

clock = time.clock()
while(True):
    clock.tick()

    img = sensor.snapshot()
    for i in range(10):
        x = (pyb.rng() % (2*img.width())) - (img.width()//2)
        y = (pyb.rng() % (2*img.height())) - (img.height()//2)
        w = (pyb.rng() % (img.width()//2))
        h = (pyb.rng() % (img.height()//2))
        r = (pyb.rng() % 127) + 128
        g = (pyb.rng() % 127) + 128
        b = (pyb.rng() % 127) + 128
    img.draw_rectangle(x, y, w, h, color = (r, g, b), thickness = 2, fill = False)
    # default settings just do one detection... change them to search the image...
    for obj in tf.classify(net, img, min_scale=1.0, scale_mul=0.8, x_overlap=0.5, y_overlap=0.5):
        print("**********\nPredictions at [x=%d,y=%d,w=%d,h=%d]" % obj.rect())
        img.draw_rectangle(obj.rect())


        # This combines the labels and confidence values into a list of tuples
        predictions_list = list(zip(labels, obj.output()))

        for i in range(len(predictions_list)):
            print("%s = %f" % (predictions_list[i][0], predictions_list[i][1]))

    print(clock.fps(), "fps")
    #目标判断及框取
   # img.draw_rectangle(blob.rect())#如果识别到颜色就框起来
    #img.draw_cross(blob.cx(), blob.cy())#draw_cross:在中心画十字架；blob.cx(), blob.cy：色块中心坐标+

在摄像头识别物体时，可以使用 画图 功能框出物体和 画十字架确定中心位置吗？

识别4种不同的垃圾 通过判断是哪一类垃圾输出给连接舵机的机械臂来抓取 这样的设计是否可？