CorgiDude บอร์ด Image Classification โดยใช้ Transfer Learning MobileNetV1

{
    "model" : {
        "type":                 "Classifier",
        "architecture":         "MobileNet7_5",
        "input_size":           224,
        "fully-connected":      [100,50],
        "labels":               [],
        "dropout" :         0.2
    },
     "weights" : {
            "full":                   "",
            "backend":               "imagenet",
            "save_bottleneck":      false

    },
    "train" : {
        "actual_epoch":         10,
        "train_image_folder":   "/content/datasets",
        "train_times":          4,
        "valid_image_folder":   "/content/datasets",
        "valid_times":          4,
        "valid_metric":         "val_accuracy",
        "batch_size":           32,
        "learning_rate":        1e-4,
        "saved_folder":           "classifier",
        "first_trainable_layer": "dense_1",
        "augumentation":                True
    },
    "converter" : {
        "type":                   ["k210","tflite"]
    }
}

import sensor, image, lcd, time 
import KPU as kpu 

sensor.reset() 
sensor.set_pixformat(sensor.RGB565) 
sensor.set_framesize(sensor.QVGA) 
sensor.set_windowing((224, 224)) 
sensor.set_vflip(1) 

lcd.init(type=2, freq=20000000, color=lcd.BLACK) 
lcd.rotation(2) 

labels=['cats','dogs'] #number of labels should match the number of labels the model was trained with 
task = kpu.load(0x200000) #change to "/sd/name_of_the_model_file.kmodel" if loading from SD card 
kpu.set_outputs(task, 0, 1, 1, 2) #the actual shape needs to match the last layer shape of your model 

while(True): 
    kpu.memtest() 
    img = sensor.snapshot() #img = img.rotation_corr(z_rotation=90.0) uncomment if need rotation correction - only present in full maixpy firmware #a = img.pix_to_ai() 
    fmap = kpu.forward(task, img) 
    plist=fmap[:] 
    pmax=max(plist) 
    max_index=plist.index(pmax) 
    a = img.draw_string(0,0, str(labels[max_index].strip()), color=(255,0,0), scale=2) 
    a = img.draw_string(0,20, str(pmax), color=(255,0,0), scale=2) 
    print((pmax, labels[max_index].strip())) 
    a = lcd.display(img) 
    a = kpu.deinit(task)