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?
-
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
-
据说是性能不够.......