提出的YOLO-World在大型数据集上的预训练模型,展现了强大的零样本的迁移学习性能,并且在LVIS数据集上达到了35.4AP和52FPS,预训练后的YOLO-World可以轻松适应于下游任务,比如开集实例分割和目标检测,此外YOLO-World的预训练权重和代码将会开源并利用在更多现实场景中 相关工作 传统目标检测 目前流行的目标检测研究关注于...
PYTHONPATH=./ python3 deploy/export_onnx.py ./configs/pretrain/yolo_world_v2_s_vlpan_bn_2e-3_100e_4x8gpus_obj365v1_goldg_train_1280ft_lvis_minival.py ./yolo_world_v2_s.pth --model-only --device 'cpu' --custom-text '' --opset 11 --model-only --backend ncnn 这样,代码就能...
world_size = world_size """YOLO World v8头部。""" def loss(self, img_feats: Tuple[Tensor], txt_feats: Tensor, batch_data_samples: Union[list, dict]) -> dict: """对上游网络的特征执行前向传播和损失计算""" outs = self(img_feats, txt_feats) # 快速版本 loss_inputs = outs + (...
paramwise_cfg:Optional[dict] =None) ->None:# 调用父类的初始化函数super().__init__(optim_wrapper_cfg, paramwise_cfg)# 从参数配置中弹出'base_total_batch_size',默认值为64self.base_total_batch_size = self.paramwise_cfg.pop('base_total_batch_size',64)# 定义一个方法,用于为模型创建优化...
此时的world_size=1,随后进入do_train方法中,该方法位于\ultralytics\engine\trainer.py中,这也是最终执行训练的地方。 AI检测代码解析 def _do_train(self, world_size=1): """Train completed, evaluate and plot if specified by arguments.""" if world_size > 1: self._setup_ddp(world_size) self...
last_stage_out_channels // 2 // 32] base_lr = 2e-4 weight_decay = 0.05 train_batch_size_per_gpu = 8 load_from = 'pretrained_models/yolo_world_m_clip_base_dual_vlpan_2e-3adamw_32xb16_100e_o365_goldg_train_pretrained-2b7bd1be.pth' persistent_workers = False # Polygon2Mask ...
base_lr =2e-4weight_decay =0.05train_batch_size_per_gpu =8load_from ='pretrained_models/yolo_world_l_clip_base_dual_vlpan_2e-3adamw_32xb16_100e_o365_goldg_train_pretrained-0e566235.pth'persistent_workers =False# Polygon2Mask 参数设置downsample_ratio =4mask_overlap =Falseuse_mask2refine...
ultralytics 8.1.21 Add YOLOv8-World-v2 models (ultralytics#8580) Mar 4, 2024 custom add img export Mar 14, 2024 docker ultralytics 8.1.18 add cmake for building onnxsim on aarch64 (ultra… Feb 23, 2024 docs Add Ultralytics HUB Cloud Training banner to Docs (ultralytics#8656) ...
[4, 8, _base_.last_stage_out_channels // 2 // 32] base_lr = 2e-3 weight_decay = 0.05 / 2 train_batch_size_per_gpu = 16 # 模型设置 model = dict( type='YOLOWorldDetector', mm_neck=True, num_train_classes=num_training_classes, num_test_classes=num_classes, data_preprocessor=...
Experience seamless AI development withUltralytics HUB⭐, the ultimate platform for building, training, and deploying computer vision models. Visualize datasets, train YOLOv3, YOLOv5 and YOLOv8 🚀 models, and deploy them to real-world applications without writing any code. Transform images into ...