为什么我在edge impluse上训练的数字识别模型准确率有92%，但是用在openmv的准确率非常低呢？

[lx43]

import sensor, time, ml, uos, gc

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 = None
labels = None

try:
    # load the model, alloc the model file on the heap if we have at least 64K free after loading
    net = ml.Model("trained.tflite", load_to_fb=uos.stat('trained.tflite')[6] > (gc.mem_free() - (64*1024)))
except Exception as e:
    print(e)
    raise Exception('Failed to load "trained.tflite", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(e) + ')')

try:
    labels = [line.rstrip('\n') for line in open("labels.txt")]
except Exception as e:
    raise Exception('Failed to load "labels.txt", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(e) + ')')

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

    img = sensor.snapshot()

    predictions_list = list(zip(labels, net.predict([img])[0].flatten().tolist()))

    for i in range(len(predictions_list)):
        print("%s = %f" % (predictions_list[i][0], predictions_list[i][1]))
    #time.sleep(1)
    print(clock.fps(), "fps")
这是edge impluse生成的代码

数据集大概是这样子的，每个数字都拍了100多张。

edge impluse上训练的结果

这是openmv上识别的准确率

[loqf]

据说是性能不够.......

[kidswong999]

你可以试试：https://github.com/SingTown/openmv_tensorflow_training_scripts/tree/main/mnist

[avqs]

你这个是分类模型，物体放的位置必须和你数据集一样才能识别。这个你应该要用目标检测模型的，看官方教程的交通指示牌目标检测的例程重新训练模型