不过这里这个类写的比较简单,若进行复杂的二级分类,可以根据自己的实际任务可以改写,这里代码不唯一。这里的功能和torch_utils.py中的load_classifier函数功能相似。 本质上的实现理念就是:(b,c1,w,h) -> (b, c2) 参考代码: class Classify(nn.Module):# Classification head, i.e. x(b,c1,20,20) to x...
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative from models.experimental import attempt_load from utils.datasets import LoadImages, LoadStreams from utils.general import apply_classifier, check_img_size, check_imshow, check_requirements, check_suffix, colorstr, \ increment_path, non_...
model.half() # to FP16 if classify: # second-stage classifier modelc = load_classifier(name='resnet50', n=2) # initialize modelc.load_state_dict(torch.load('resnet50.pt', map_location=device)['model']).to(device).eval() elif onnx: check_requirements(('onnx', 'onnxruntime'))...
map_location=device)# load FP32 modelstride=int(model.stride.max())# model strideimgsz=check_img_size(imgsz,s=stride)# check img_sizeifhalf:model.half()# to FP16# Second-stage classifierclassify=Falseifclassify:modelc=load_classifier(name='resnet101',n=2)# initializemodelc.load_state_...
from utils.general import check_img_size, check_requirements, check_imshow,non_max_suppression, apply_classifier, \ scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path from utils.plots import plot_one_box from utils.torch_utils import select_device, load_classifier, time_synchro...
# Second-stage classifier classify = False if classify: modelc = load_classifier(name='resnet101', n=2) # initialize modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval()
运行数据记录于 https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2。准确度 值为ImageNet-1k数据集上的单模型单尺度。通过python classify/val.py --data ../datasets/imagenet --img 224进行复制。使用Google Colab Pro V100 High-RAM实例得出的100张推理图像的平均速度。通过 python classify/val.py --...
xyxy2xywh, strip_optimizer, set_logging, increment_pathfromutils.plotsimportplot_one_boxfromutils.torch_utilsimportselect_device, load_classifier, time_synchronized device= select_device('') augment=False conf_thres=0.15iou_thres=0.25model= attempt_load('yolov5s.pt', map_location=device) ...
classify=Falseifclassify:modelc=load_classifier(name='resnet101',n=2)# initialize modelc.load_state_dict(torch.load('weights/resnet101.pt',map_location=device)['model']).to(device).eval()# Set Dataloader vid_path,vid_writer=None,Noneifwebcam:view_img=check_imshow()cudnn.benchmark=True...
yolo_classifier = torch.hub.load( File "C:\Users\vAppData\Local\miniconda3\envs\env_3_9\lib\site-packages\torch\hub.py", line 542, in load model = _load_local(repo_or_dir, model, *args, **kwargs) File "C:\Users\AppData\Local\miniconda3\envs\env_3_9\lib\site-packages\torch...