url = 'https://stock.adobe.com/in/search/images?k=car%20scratch'
向 url 发出请求
r = requests.get(url)
创建我们的汤
soup = BeautifulSoup(r.文本, 'html.parser')
print(soup.title.text)

images = soup.find_all('img')
for images[-1] 中的图像：
name = image['alt']
link = image['src']
with open(name.replace(' ', '-').将('/', '') + '.jpg', 'wb') 替换为 f:
im = requests.get(link)
f.write(im.content)

# importing libraries
import pandas as pd
import numpy as np
import cv2
import os
import re
from PIL import Image
import albumentations as A
from albumentations.pytorch.transforms import ToTensorV2
import torch
import torchvision
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
from torchvision.models.detection import FasterRCNN
from torchvision.models.detection.rpn import AnchorGenerator
from torch.utils.data import DataLoader, Dataset
from torch.utils.data.sampler import SequentialSampler
from matplotlib import pyplot as plt

image_ids = train_df['image_id'].unique()
print(len(image_ids))
valid_ids = image_ids[-10:]
train_ids = image_ids[:-10]
# valid and train df
valid_df = train_df[train_df['image_id'].isin(valid_ids)]
train_df = train_df[train_df['image_id'].isin(train_ids)]

class ScratchDataset(Dataset):
    def __init__(self, dataframe, image_dir, transforms=None):
        super().__init__()
        self.image_ids = dataframe['image_id'].unique()
        self.df = dataframe
        self.image_dir = image_dir
        self.transforms = transforms
    def __getitem__(self, index: int):
        image_id = self.image_ids[index]
        records = self.df[self.df['image_id'] == image_id]
        image = cv2.imread(f'{self.image_dir}/{image_id}.jpg', cv2.IMREAD_COLOR)
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB).astype(np.float32)
        image /= 255.0
        boxes = records[['x', 'y', 'w', 'h']].values
        boxes[:, 2] = boxes[:, 0] + boxes[:, 2]
        boxes[:, 3] = boxes[:, 1] + boxes[:, 3]
        area = (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 2]-boxes[:, 0])
        area = torch.as_tensor(area, dtype=torch.float32)
        # there is only one class
        labels = torch.ones((records.shape[0],), dtype=torch.int64)
        # suppose all instances are not crowd
        iscrowd = torch.zeros((records.shape[0],), dtype=torch.int64)
        target = {}
        target['boxes']=boxes
        target['labels']=labels
        target['image_id']=torch.tensor([index])
        target['area']=area
        target['iscrowd']=iscrowd
        if self.transforms:
            sample = {
                'image':image,
                'bboxes': target['boxes'],
                'labels': labels
            }
            sample = self.transforms(**sample)
            image = sample['image']
            target['boxes'] = torch.tensor(sample['bboxes'])
        return image, target, image_id
    def __len__(self) -> int:
        return self.image_ids.shape[0]

# Albumenations
def get_train_transform():
    return A.Compose([
        A.Flip(0.5),
        ToTensorV2(p=1.0)
    ], bbox_params={'format':'pascal_voc', 'label_fields':['labels']})
def get_valid_transform():
    return A.Compose([
        ToTensorV2(p=1.0)
    ], bbox_params={'format': 'pascal_voc', 'label_fields':['labels']})

# load a model pre-trained on COCO
model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
num_classes = 2 # 1 class scratch+ background
# get number of input features for the classifier
in_features = model.roi_heads.box_predictor.cls_score.in_features
# replace th epre-trained head with a new one
model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)

class Averager:
    def __init__(self):
        self.current_total = 0.0
        self.iterations = 0.0
    def send(self, value):
        self.current_total += value
        self.iterations += 1
    @property
    def value(self):
        if self.iterations == 0:
            return 0
        else:
            return 1.0 * self.current_total/ self.iterations
    def reset(self):
        self.current_total = 0.0
        self.iterations = 0.0
def collate_fn(batch):
    return tuple(zip(*batch))
train_dataset = WheatDataset(train_df, DIR_TRAIN, get_train_transform())
valid_dataset = WheatDataset(valid_df, DIR_TRAIN, get_valid_transform())
# split the dataset in train and test set
indices = torch.randperm(len(train_dataset)).tolist()
train_data_loader = DataLoader(
    train_dataset,
    batch_size=16,
    shuffle=False,
    num_workers=4,
    collate_fn=collate_fn
)
valid_data_loader = DataLoader(
    valid_dataset,
    batch_size=8,
    shuffle=False,
    num_workers=4,
    collate_fn=collate_fn
)

device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
images, targets, image_ids = next(iter(train_data_loader))
model.to(device)
params = [p for p in model.parameters() if p.requires_grad]
optimizer = torch.optim.SGD(params, lr=0.005, momentum=0.9, 
                            weight_decay=0.0005)
# lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=3, gamma=0.1)
lr_scheduler = None
num_epochs = 2
loss_hist = Averager()
itr=1
for epoch in range(num_epochs):
    loss_hist.reset()
    for images, targets, image_ids, in train_data_loader:
        images = list(image.to(device) for image in images)
        targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
        loss_dict = model(images, targets)
        losses = sum(loss for loss in loss_dict.values())
        loss_value = losses.item()
        loss_hist.send(loss_value)
        optimizer.zero_grad()
        losses.backward()
        optimizer.step()
        if itr % 50 == 0:
            print(f'Iteration #{itr} loss: {loss_value}')
        itr += 1
    # update the learning rate
    if lr_scheduler is not None:
        lr_scheduler.step()
    print(f'Epoch #{epoch} loss: {loss_hist.value}')

model = torch.hub.load('ultralytics/yolov5','yolov5s')

!cd yolov5 && python train.py --img 320 --batch 16 --epochs 50 --data carScr_up.yaml --weights last.pt

!cd yolov5 && python train.py --img 320 --batch 32 --epochs 10 --data carScr_up.yaml --weights yolov5s.pt --cache --evolve