比赛地址：https://www.kaggle.com/c/leaf-classification/rules
完整代码：https://github.com/SPECTRELWF/kaggle_competition
个人主页：liuweifeng.top:8090

比赛题目：对树叶的类别进行分类，树叶总共99个类别，树叶的图片如下：

我也不知道怎么分类，反正总共有99中类别的树叶。下载到的数据集解压后如下：

image里面存了所有的树叶图像，train.csv是训练文件的标号以及类别，后面有一堆的特征，我没用到，因为比赛已经结束了，我只是纯纯的拿了练习下CNN。test.csv文件是测试数据的标号，sample_submission.csv文件是提交样例，长这样：

第一列是id，后面的99列是对应的每个类别的概率，分类结果加上softmax就行。

思路：

直接使用的基于ImageNet预训练的resnet101，微调一下。

预处理

将训练集的id和label写到一个txt文件中，测试集的id写入另一个txt文件：

# !/usr/bin/python3
# -*- coding:utf-8 -*-
# Author:WeiFeng Liu
# @Time: 2021/12/8 上午10:27import os
import pandas as pd
classes = ['Acer_Capillipes', 'Acer_Circinatum', 'Acer_Mono', 'Acer_Opalus', 'Acer_Palmatum', 'Acer_Pictum', 'Acer_Platanoids', 'Acer_Rubrum', 'Acer_Rufinerve', 'Acer_Saccharinum', 'Alnus_Cordata', 'Alnus_Maximowiczii', 'Alnus_Rubra', 'Alnus_Sieboldiana', 'Alnus_Viridis', 'Arundinaria_Simonii', 'Betula_Austrosinensis', 'Betula_Pendula', 'Callicarpa_Bodinieri', 'Castanea_Sativa', 'Celtis_Koraiensis', 'Cercis_Siliquastrum', 'Cornus_Chinensis', 'Cornus_Controversa', 'Cornus_Macrophylla', 'Cotinus_Coggygria', 'Crataegus_Monogyna', 'Cytisus_Battandieri', 'Eucalyptus_Glaucescens', 'Eucalyptus_Neglecta', 'Eucalyptus_Urnigera', 'Fagus_Sylvatica', 'Ginkgo_Biloba', 'Ilex_Aquifolium', 'Ilex_Cornuta', 'Liquidambar_Styraciflua', 'Liriodendron_Tulipifera', 'Lithocarpus_Cleistocarpus', 'Lithocarpus_Edulis', 'Magnolia_Heptapeta', 'Magnolia_Salicifolia', 'Morus_Nigra', 'Olea_Europaea', 'Phildelphus', 'Populus_Adenopoda', 'Populus_Grandidentata', 'Populus_Nigra', 'Prunus_Avium', 'Prunus_X_Shmittii', 'Pterocarya_Stenoptera', 'Quercus_Afares', 'Quercus_Agrifolia', 'Quercus_Alnifolia', 'Quercus_Brantii', 'Quercus_Canariensis', 'Quercus_Castaneifolia', 'Quercus_Cerris', 'Quercus_Chrysolepis', 'Quercus_Coccifera', 'Quercus_Coccinea', 'Quercus_Crassifolia', 'Quercus_Crassipes', 'Quercus_Dolicholepis', 'Quercus_Ellipsoidalis', 'Quercus_Greggii', 'Quercus_Hartwissiana', 'Quercus_Ilex', 'Quercus_Imbricaria', 'Quercus_Infectoria_sub', 'Quercus_Kewensis', 'Quercus_Nigra', 'Quercus_Palustris', 'Quercus_Phellos', 'Quercus_Phillyraeoides', 'Quercus_Pontica', 'Quercus_Pubescens', 'Quercus_Pyrenaica', 'Quercus_Rhysophylla', 'Quercus_Rubra', 'Quercus_Semecarpifolia', 'Quercus_Shumardii', 'Quercus_Suber', 'Quercus_Texana', 'Quercus_Trojana', 'Quercus_Variabilis', 'Quercus_Vulcanica', 'Quercus_x_Hispanica', 'Quercus_x_Turneri', 'Rhododendron_x_Russellianum', 'Salix_Fragilis', 'Salix_Intergra', 'Sorbus_Aria', 'Tilia_Oliveri', 'Tilia_Platyphyllos', 'Tilia_Tomentosa', 'Ulmus_Bergmanniana', 'Viburnum_Tinus', 'Viburnum_x_Rhytidophylloides', 'Zelkova_Serrata']train_txt = open('train.txt','w')
train_csv = pd.read_csv(r'leaf-classification/train.csv')
ids = train_csv['id']
species = train_csv['species']for i in range(len(ids)):train_txt.write(str(ids[i]))train_txt.write(' ')train_txt.write(str(classes.index(str(species[i]))))train_txt.write('\n')
train_txt.close()test_txt = open('test.txt','w')
test_csv = pd.read_csv(r'leaf-classification/test.csv')
ids = test_csv['id']
for i in range(len(ids)):test_txt.write(str(ids[i]))test_txt.write('\n')
test_txt.close()

模型resnet101

# !/usr/bin/python3
# -*- coding:utf-8 -*-
# Author:WeiFeng Liu
# @Time: 2021/12/8 上午10:24
import torch
import torchvision.models
import torchvision.transforms as transforms
import torch.nn as nn
import torchvision.models as modelsclass resnet101(nn.Module):def __init__(self, num_classes=1000):super(resnet101, self).__init__()self.num_classes = num_classesself.feature_extract = torchvision.models.resnet101(pretrained=True)self.net = nn.Sequential(nn.Linear(1000, 512),nn.ReLU(inplace=True),nn.Dropout(0.5),nn.Linear(512, 256),nn.ReLU(inplace=True),nn.Dropout(0.5),nn.Linear(256, num_classes),)def forward(self, x):x = self.feature_extract(x)x = self.net(x)return x# x = torch.randn((1,3,224,224))
# net = resnet101(num_classes=99)
# print(net)
# print(net(x).shape)

dataloader

# !/usr/bin/python3
# -*- coding:utf-8 -*-
# Author:WeiFeng Liu
# @Time: 2021/12/8 上午10:24
import numpy as np
import torch.utils.data as data
import torch
import torchvision.transforms as transforms
from PIL import Image
data_root = r'leaf-classification/images/'class leaf_Dataset(data.Dataset):def __init__(self,is_train=True,transform=None):self.is_train = is_trainself.transform = transformself.images = []self.labels = []if is_train:file = open('train.txt','r')lines = file.readlines()for line in lines:res = line[:-1]image = res.split(' ')[0]label = int(res.split(' ')[1])self.images.append(image)self.labels.append(label)print(self.images)print(self.labels)def __len__(self):return len(self.images)def __getitem__(self, index):image_name = self.images[index] + '.jpg'image_path = data_root + image_nameimg = Image.open(image_path).convert('RGB')# print(img)img = self.transform(img)label = self.labels[index]label = torch.from_numpy(np.array(label))return img, labeltransforms = transforms.Compose([transforms.Resize((224,224)),transforms.ToTensor()
])

# !/usr/bin/python3
# -*- coding:utf-8 -*-
# Author:WeiFeng Liu
# @Time: 2021/12/8 上午10:25"""
使用imagenet预训练的rennet101来在树叶数据集上面进行微调
"""
import torch
import torchvision.transforms as transforms
from dataset import leaf_Dataset
import torch.utils.data as data
import torch.optim as optim
import torch.nn as nn
from resnet import resnet101
#使用Adam优化器来训练网络，不冻结参数# 设置hyperparameterepoch = 200
lr = 1e-3
b1 = 0.9
b2 = 0.999
device = torch.device('cuda:0')
train_loss = []
# 初始化网络模型
net = resnet101(num_classes=99)
net.to(device)# load data
transforms = transforms.Compose([transforms.Resize((224,224)),transforms.RandomHorizontalFlip(),transforms.RandomVerticalFlip(),transforms.ToTensor(),
])
data = leaf_Dataset(is_train=True,transform=transforms)
dataloader = torch.utils.data.DataLoader(data,batch_size=64,shuffle=True)loss_func = nn.CrossEntropyLoss()
opt = torch.optim.Adam(net.parameters(),lr=lr,betas=(b1,b2))for epoch in range(1,epoch + 1):for i, (x,y) in enumerate(dataloader):x = x.to(device)y = y.to(device)pred = net(x)loss = loss_func(pred,y)opt.zero_grad()loss.backward()opt.step()train_loss.append(loss.item())print("epoch: %d   batch_idx:%d   loss:%.3f" %(epoch,i,loss.item()))torch.save(net.state_dict(),'model/epoch:%d'%epoch + '.pth')
from utils import plot_curve
plot_curve(train_loss)

loss

将预测结果写入要提交的文件

# !/usr/bin/python3
# -*- coding:utf-8 -*-
# Author:WeiFeng Liu
# @Time: 2021/12/8 下午5:42
import torch
import torchvision.transforms as transforms
import numpy as np
import os
from PIL import Image
from resnet import resnet101
import torch.nn.functional as Fimage_path = r'leaf-classification/images'
f = open('test.txt','r')
tmp = f.readlines()
test_file = []
for i in tmp:i = i[:-1]test_file.append(i+'.jpg')
print(test_file)device = torch.device('cuda:0')
net = resnet101(num_classes=99)
print('load weight........')
net.load_state_dict(torch.load('model/epoch:200.pth'))
net.to(device)
net.eval()
transformss = transforms.Compose([transforms.Resize((224,224)),transforms.ToTensor()
])
res = []
with torch.no_grad():for image in test_file:img = Image.open(os.path.join(image_path,image)).convert('RGB')img = transformss(img)img = torch.unsqueeze(img,dim=0)img = img.to(device)# print(img.shape)pred = net(img)pred = F.softmax(pred).flatten()pred = pred.cpu().numpy()print(pred)res.append(pred)np.savetxt("result.csv",res,delimiter = ',')

实战Kaggle比赛（1）：树叶分类相关推荐

沐神《动手学深度实战Kaggle比赛：狗的品种识别（ImageNet Dogs）
沐神<动手学深度学习>飞桨版课程公开啦! hello各位飞桨的开发者,大家好!李沐老师的<动手学深度学习>飞桨版课程已经公开啦.本课程由PPSIG和飞桨工程师共同建设,将原书中 ...
动手学深度学习：3.16 实战Kaggle比赛：房价预测
3.16 实战Kaggle比赛:房价预测作为深度学习基础篇章的总结,我们将对本章内容学以致用.下面,让我们动手实战一个Kaggle比赛:房价预测.本节将提供未经调优的数据的预处理.模型的设计和超参数 ...
实战Kaggle比赛----预测房价(多层感知机)
文章目录实战Kaggle比赛----预测房价下载和缓存数据集 Kaggle简介访问和读取数据集数据预处理标准正态化.缺失值填充.离散值one-hot编码小栗子帮助理解训练 KKK折交叉验 ...
《动手深度学习》4.10. 实战Kaggle比赛：预测房价
4.10. 实战Kaggle比赛:预测房价本节内容预览数据下载和缓存数据集访问和读取数据集使用pandas读入并处理数据数据预处理处理缺失值&对数值类数据标准化处理离散值-on ...
04.10. 实战Kaggle比赛：预测房价
4.10. 实战Kaggle比赛:预测房价详细介绍数据预处理.模型设计和超参数选择. 通过亲身实践,你将获得一手经验,这些经验将有益数据科学家的职业成长. import hashlib import ...
超详解pytorch实战Kaggle比赛：房价预测
详解pytorch实战Kaggle比赛:房价预测教程名称教程地址机器学习/深度学习 [李宏毅]机器学习/深度学习国语教程(双语字幕) 生成对抗网络 [李宏毅]生成对抗网络国语教程(双语字幕) 目 ...
实战Kaggle比赛：预测房价
文章目录实战Kaggle比赛:预测房价 1 - 下载和缓存数据集 2 - 访问和读取数据集 3 - 数据预处理 4 - 训练 5 - K折交叉验证 6 - 模型选择 7 - 提交你的Kaggle预测 ...
深度学习+pytorch实战Kaggle比赛（一）——房价预测
参考书籍<动手学深度学习(pytorch版),参考网址为: https://zh-v2.d2l.ai/chapter_multilayer-perceptrons/kaggle-house-pr ...
pytorch学习笔记（十四）：实战Kaggle比赛——房价预测
文章目录 1. Kaggle比赛 2. 获取和读取数据集 3. 预处理数据 4. 训练模型 5. KKK折交叉验证 6. 模型选择 7. 预测并在Kaggle提交结果 1. Kaggle比赛 Kagg ...

实战Kaggle比赛（1）：树叶分类

思路：

预处理

模型resnet101

dataloader

loss

将预测结果写入要提交的文件

实战Kaggle比赛（1）：树叶分类相关推荐

最新文章

热门文章