train_num_workers=4# 加载COCO预训练权重 load_from='https://download.openmmlab.com/MMYOLO/v0/yolov5/yolov5_s-v61_syncbn_fast_8xb16-300e_coco/yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700-86e02187.pth'# noqa model=dict(# 固定整个 backbone 权重,不进行训练 backbone=dict(f...
上述配置中,默认是没有加载预训练权重的,大家可以在configs/yolov5/yolov5_s-v61_syncbn_fast_1xb4-300e_balloon.py快速添加如下代码: load_from = 'https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_s-v61_syncbn_fast_8xb16-300e_coco/yolov5_s-v61_syncbn_fast_8xb16-300e_coco_2022091...
默认从硬盘读取pre_transform=[# 训练数据读取流程dict(type='LoadImageFromFile',# 第 1 个流程,从文件路径里加载图像file_client_args=file_client_args),# 文件读取后端的配置,默认从硬盘读取dict(type='LoadAnnotations',# 第 2
import torchvision.transforms as transforms from sklearn import svm from sklearn.externals import joblib from sklearn.preprocessing import StandardScaler # Step 1: Selective Search to propose regions def selective_search(img): ss = cv2.ximgproc.segmentation.createSelectiveSearchSegmentation() ss.setBase...
dict(type='LoadImageFromFile', file_client_args=_base_.file_client_args), dict(type='YOLOv5KeepRatioResize', scale=img_scale), dict( type='LetterResize', scale=img_scale, allow_scale_up=False, pad_val=dict(img=114)), dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'),...
dataset_type='YOLOv5CocoDataset'train_pipeline = [# 训练数据读取流程dict( type='LoadImageFromFile',# 第 1 个流程,从文件路径里加载图像file_client_args=file_client_args),# 文件读取后端的配置,默认从硬盘读取dict(type='LoadAnnotations',# 第 2 个流程,对于当前图像,加载它的注释信息with_bbox=True...
dict(type='LoadImageFromFile', file_client_args=_base_.file_client_args), dict( type='LoadAnnotations', with_bbox=True, with_mask=True, mask2bbox=use_mask2refine) ] last_transform = [ # Delete gt_masks to avoid more computation dict(type='RemoveDataElement', keys=['gt_masks']), ...
test_pipeline=[dict(type='LoadImageFromFile',file_client_args={{_base_.file_client_args}}),dict(type='YOLOv5KeepRatioResize',scale=img_scale),dict(type='LetterResize',scale=img_scale,allow_scale_up=False,pad_val=dict(img=114)),dict(type='LoadAnnotations',with_bbox=True,_scope_='mmdet...
(type='LoadImageFromFile', file_client_args=_base_.file_client_args), dict(type='YOLOv5KeepRatioResize', scale=img_scale), dict( type='LetterResize', scale=img_scale, allow_scale_up=False, pad_val=dict(img=114)), dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'), ...
from ultralytics import YOLOv10 model = YOLOv10() # If you want to finetune the model with pretrained weights, you could load the # pretrained weights like below # model = YOLOv10.from_pretrained('jameslahm/yolov10{n/s/m/b/l/x}') # or # wget https://github.com/THU-MIG/yolo...