Fruits 360 基于CNN实现果蔬识别系统_fruits-360是用什么模型训练的

作者：小小林熬夜学编程 | 2024-06-09 15:55:40

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fruits-360是用什么模型训练的

先看最终效果：

项目主要分为三部分：


1、数据集解析处理
2、模型训练
3、推理计算与GUI可视化

本文使用的数据集是开源的果蔬数据集Fruits 360，下载后截图如下所示：

其中：

Test 表示测试数据集目录

Training 表示训练数据集目录

接下来各自看下对应的数据集情况。

Training 目录截图如下所示：

Test目录截图如下所示：

可以看到：训练集和测试集都是提前划分好的，每个目录下面都有131个子目录，也就是该数据集中一共是131个类别，接下来我们详细统计下数据详情，代码如下：


def lookDataset():
    '''
    查看数据集情况
    '''
    dataDir = "data/train/"
    train_dict = {}
    train_num = 0
    for one_label in os.listdir(dataDir):
        oneDir = dataDir + one_label + "/"
        one_num = len(os.listdir(oneDir))
        train_dict[one_label] = one_num
        train_num += one_num
    print("Total Train Number: ", train_num)
    with open("train_num.json", "w") as f:
        f.write(json.dumps(train_dict))
    dataDir = "data/test/"
    test_dict = {}
    test_num = 0
    for one_label in os.listdir(dataDir):
        oneDir = dataDir + one_label + "/"
        one_num = len(os.listdir(oneDir))
        test_dict[one_label] = one_num
        test_num += one_num
    print("Total Test Number: ", test_num)
    with open("test_num.json", "w") as f:
        f.write(json.dumps(test_dict))

结果输出如下：

训练集-测试集数据量如下：


Total Train Number:  67692
Total Test Number:  22688

其中，我对其各个类别数据量也进行了统计，测试集详情如下所示：


{
	"Apple Braeburn": 164,
	"Apple Crimson Snow": 148,
	"Apple Golden 1": 160,
	"Apple Golden 2": 164,
	"Apple Golden 3": 161,
	"Apple Granny Smith": 164,
	"Apple Pink Lady": 152,
	"Apple Red 1": 164,
	"Apple Red 2": 164,
	"Apple Red 3": 144,
	"Apple Red Delicious": 166,
	"Apple Red Yellow 1": 164,
	"Apple Red Yellow 2": 219,
	"Apricot": 164,
	"Avocado": 143,
	"Avocado ripe": 166,
	"Banana": 166,
	"Banana Lady Finger": 152,
	"Banana Red": 166,
	"Beetroot": 150,
	"Blueberry": 154,
	"Cactus fruit": 166,
	"Cantaloupe 1": 164,
	"Cantaloupe 2": 164,
	"Carambula": 166,
	"Cauliflower": 234,
	"Cherry 1": 164,
	"Cherry 2": 246,
	"Cherry Rainier": 246,
	"Cherry Wax Black": 164,
	"Cherry Wax Red": 164,
	"Cherry Wax Yellow": 164,
	"Chestnut": 153,
	"Clementine": 166,
	"Cocos": 166,
	"Corn": 150,
	"Corn Husk": 154,
	"Cucumber Ripe": 130,
	"Cucumber Ripe 2": 156,
	"Dates": 166,
	"Eggplant": 156,
	"Fig": 234,
	"Ginger Root": 99,
	"Granadilla": 166,
	"Grape Blue": 328,
	"Grape Pink": 164,
	"Grape White": 166,
	"Grape White 2": 166,
	"Grape White 3": 164,
	"Grape White 4": 158,
	"Grapefruit Pink": 166,
	"Grapefruit White": 164,
	"Guava": 166,
	"Hazelnut": 157,
	"Huckleberry": 166,
	"Kaki": 166,
	"Kiwi": 156,
	"Kohlrabi": 157,
	"Kumquats": 166,
	"Lemon": 164,
	"Lemon Meyer": 166,
	"Limes": 166,
	"Lychee": 166,
	"Mandarine": 166,
	"Mango": 166,
	"Mango Red": 142,
	"Mangostan": 102,
	"Maracuja": 166,
	"Melon Piel de Sapo": 246,
	"Mulberry": 164,
	"Nectarine": 164,
	"Nectarine Flat": 160,
	"Nut Forest": 218,
	"Nut Pecan": 178,
	"Onion Red": 150,
	"Onion Red Peeled": 155,
	"Onion White": 146,
	"Orange": 160,
	"Papaya": 164,
	"Passion Fruit": 166,
	"Peach": 164,
	"Peach 2": 246,
	"Peach Flat": 164,
	"Pear": 164,
	"Pear 2": 232,
	"Pear Abate": 166,
	"Pear Forelle": 234,
	"Pear Kaiser": 102,
	"Pear Monster": 166,
	"Pear Red": 222,
	"Pear Stone": 237,
	"Pear Williams": 166,
	"Pepino": 166,
	"Pepper Green": 148,
	"Pepper Orange": 234,
	"Pepper Red": 222,
	"Pepper Yellow": 222,
	"Physalis": 164,
	"Physalis with Husk": 164,
	"Pineapple": 166,
	"Pineapple Mini": 163,
	"Pitahaya Red": 166,
	"Plum": 151,
	"Plum 2": 142,
	"Plum 3": 304,
	"Pomegranate": 164,
	"Pomelo Sweetie": 153,
	"Potato Red": 150,
	"Potato Red Washed": 151,
	"Potato Sweet": 150,
	"Potato White": 150,
	"Quince": 166,
	"Rambutan": 164,
	"Raspberry": 166,
	"Redcurrant": 164,
	"Salak": 162,
	"Strawberry": 164,
	"Strawberry Wedge": 246,
	"Tamarillo": 166,
	"Tangelo": 166,
	"Tomato 1": 246,
	"Tomato 2": 225,
	"Tomato 3": 246,
	"Tomato 4": 160,
	"Tomato Cherry Red": 164,
	"Tomato Heart": 228,
	"Tomato Maroon": 127,
	"Tomato not Ripened": 158,
	"Tomato Yellow": 153,
	"Walnut": 249,
	"Watermelon": 157
}

训练集详情如下所示：


{
	"Apple Braeburn": 492,
	"Apple Crimson Snow": 444,
	"Apple Golden 1": 480,
	"Apple Golden 2": 492,
	"Apple Golden 3": 481,
	"Apple Granny Smith": 492,
	"Apple Pink Lady": 456,
	"Apple Red 1": 492,
	"Apple Red 2": 492,
	"Apple Red 3": 429,
	"Apple Red Delicious": 490,
	"Apple Red Yellow 1": 492,
	"Apple Red Yellow 2": 672,
	"Apricot": 492,
	"Avocado": 427,
	"Avocado ripe": 491,
	"Banana": 490,
	"Banana Lady Finger": 450,
	"Banana Red": 490,
	"Beetroot": 450,
	"Blueberry": 462,
	"Cactus fruit": 490,
	"Cantaloupe 1": 492,
	"Cantaloupe 2": 492,
	"Carambula": 490,
	"Cauliflower": 702,
	"Cherry 1": 492,
	"Cherry 2": 738,
	"Cherry Rainier": 738,
	"Cherry Wax Black": 492,
	"Cherry Wax Red": 492,
	"Cherry Wax Yellow": 492,
	"Chestnut": 450,
	"Clementine": 490,
	"Cocos": 490,
	"Corn": 450,
	"Corn Husk": 462,
	"Cucumber Ripe": 392,
	"Cucumber Ripe 2": 468,
	"Dates": 490,
	"Eggplant": 468,
	"Fig": 702,
	"Ginger Root": 297,
	"Granadilla": 490,
	"Grape Blue": 984,
	"Grape Pink": 492,
	"Grape White": 490,
	"Grape White 2": 490,
	"Grape White 3": 492,
	"Grape White 4": 471,
	"Grapefruit Pink": 490,
	"Grapefruit White": 492,
	"Guava": 490,
	"Hazelnut": 464,
	"Huckleberry": 490,
	"Kaki": 490,
	"Kiwi": 466,
	"Kohlrabi": 471,
	"Kumquats": 490,
	"Lemon": 492,
	"Lemon Meyer": 490,
	"Limes": 490,
	"Lychee": 490,
	"Mandarine": 490,
	"Mango": 490,
	"Mango Red": 426,
	"Mangostan": 300,
	"Maracuja": 490,
	"Melon Piel de Sapo": 738,
	"Mulberry": 492,
	"Nectarine": 492,
	"Nectarine Flat": 480,
	"Nut Forest": 654,
	"Nut Pecan": 534,
	"Onion Red": 450,
	"Onion Red Peeled": 445,
	"Onion White": 438,
	"Orange": 479,
	"Papaya": 492,
	"Passion Fruit": 490,
	"Peach": 492,
	"Peach 2": 738,
	"Peach Flat": 492,
	"Pear": 492,
	"Pear 2": 696,
	"Pear Abate": 490,
	"Pear Forelle": 702,
	"Pear Kaiser": 300,
	"Pear Monster": 490,
	"Pear Red": 666,
	"Pear Stone": 711,
	"Pear Williams": 490,
	"Pepino": 490,
	"Pepper Green": 444,
	"Pepper Orange": 702,
	"Pepper Red": 666,
	"Pepper Yellow": 666,
	"Physalis": 492,
	"Physalis with Husk": 492,
	"Pineapple": 490,
	"Pineapple Mini": 493,
	"Pitahaya Red": 490,
	"Plum": 447,
	"Plum 2": 420,
	"Plum 3": 900,
	"Pomegranate": 492,
	"Pomelo Sweetie": 450,
	"Potato Red": 450,
	"Potato Red Washed": 453,
	"Potato Sweet": 450,
	"Potato White": 450,
	"Quince": 490,
	"Rambutan": 492,
	"Raspberry": 490,
	"Redcurrant": 492,
	"Salak": 490,
	"Strawberry": 492,
	"Strawberry Wedge": 738,
	"Tamarillo": 490,
	"Tangelo": 490,
	"Tomato 1": 738,
	"Tomato 2": 672,
	"Tomato 3": 738,
	"Tomato 4": 479,
	"Tomato Cherry Red": 492,
	"Tomato Heart": 684,
	"Tomato Maroon": 367,
	"Tomato not Ripened": 474,
	"Tomato Yellow": 459,
	"Walnut": 735,
	"Watermelon": 475
}

整体来看，划分得还是比较均衡的，基本维持在3:1的状况。

当然了，如果想要自己对数据集划分，也是可以的，这里我也同样实现了数据集随机比例划分功能，如下所示：


def random2Dataset(dataDir='data/original/',ratio=0.3):
    '''
    对原始数据集进行划分，得到：训练集和测试集
    '''
    label_list=os.listdir(dataDir)
    for one_label in label_list:
        oneDir=dataDir+one_label+'/'
        pic_list=os.listdir(oneDir)
        testNum=int(len(pic_list)*ratio)
        oneTrainDir='data/train/'+one_label+'/'
        oneTestDir='data/test/'+one_label+'/'
        if not os.path.exists(oneTrainDir):
            os.makedirs(oneTrainDir)
        if not os.path.exists(oneTestDir):
            os.makedirs(oneTestDir)
        #创建测试集
        for i in range(testNum):
            one_path=oneDir+random.choice(os.listdir(oneDir))
            name=str(len(os.listdir(oneTestDir))+1)
            new_path=oneTestDir+one_label+'_'+name+'.jpg'
            shutil.move(one_path,new_path)
        #创建训练集
        for one_pic in os.listdir(oneDir):
            one_path=oneDir+one_pic
            name=str(len(os.listdir(oneTrainDir))+1)
            new_path=oneTrainDir+one_label+'_'+name+'.jpg'
            shutil.move(one_path,new_path)

可以根据自己的实际情况进行选择。

模型层面我基于VGG主干网络进行改造，设计新的网络模型，如下所示：

可以看到：参数量缩减了很多。

我默认设置了1000次的迭代，实际观察发现：不到10次就足够了，我们可以看下训练可视化的曲线：

准确度曲线：

损失曲线：

我在模型训练结束的时候在测试集总数据集上面进行了测试，准确率达到了96.55%，如下所示：

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