图片,添加的文字,左上角坐标,字体,字号,bgr颜色,线宽img_bgr = cv2.putText(img_bgr, text, (25,50+40* i), cv2.FONT_HERSHEY_SIMPLEX,1.25, (0,0,255),3)# 保存图像cv2.imwrite('output/img_pred.jpg', img_bgr)# 载入预测结果图像img_pred = Image.open('output/img_pred.jpg') img_pred# ...
data_path=Path("data/")image_path=data_path/"pizza_steak_sushi"# If the image folder doesn't exist,download it and prepare it...ifimage_path.is_dir():print(f"{image_path} directory exists.")else:print(f"Did not find {image_path} directory, creating one...")image_path.mkdir(paren...
kernel_size=kernel_size, s=1.0)custom_transform = ContrastiveLearningViewGenerator(base_transform=base_transforms)garbage_ds = CustomDataset( list_images=glob.glob("/kaggle/input/garbage-classification/garbage_classification/*/*.jpg
image = self.transform(image) return image 作为样例,我们使用比较小的模型 ResNet18 作为主干,所以他的输入是 224x224 图像,我们按照要求设置一些参数并生成dataloader out_shape = [224, 224] kernel_size = [21, 21] # 10% of out_shape # Custom transform base_transforms = get_complete_transform(o...
n=5,classes=class_names,seed=None)# Display random images from ImageFolderCustom Datasetdisplay_...
_rs_dataset/vgg16_train_model_38.pth", map_location="cpu")) val_dataset = datasets.ImageFolder( root=r'E:\machine learning\Deep_learning\deep_learning\PyTorch\code\some_models\vgg-demo\VGG16\satelite\Satellite_Image_Classification\val', transform=custom_transform ) classes = val_dataset....
garbage_ds = CustomDataset( list_images=glob.glob("/kaggle/input/garbage-classification/garbage_classification/*/*.jpg"), transform=custom_transform ) BATCH_SZ = 128 # Build DataLoader train_dl = torch.utils.data.DataLoader( garbage_ds, ...
custom_transform=ContrastiveLearningViewGenerator(base_transform=base_transforms) garbage_ds=CustomDataset( list_images=glob.glob("/kaggle/input/garbage-classification/garbage_classification/*/*.jpg"), transform=custom_transform ) BATCH_SZ=128 # Build DataLoader ...
It's not clear what the implications of that are for the use of pretrained weights from that dataset. Any models I have trained with ImageNet are done for research purposes and one should assume that the original dataset license applies to the weights. It's best to seek legal advice if ...
_transforms=get_complete_transform(output_shape=out_shape,kernel_size=kernel_size,s=1.0)custom_transform=ContrastiveLearningViewGenerator(base_transform=base_transforms)garbage_ds=CustomDataset(list_images=glob.glob("/kaggle/input/garbage-classification/garbage_classification/*/*.jpg"),transform=custom_...