具体来说,YOLO-World使用了标准的YOLO结构,利用了预训练的CLIP文本encoder对输入文本进行编码 为了更好的视图-语义表述,我们进一步提出了PepVL-PAN来连接文本特征和图像特征 在推理阶段,上述所说的文本encoder将会被移除,而文本embedding将会进行参数重构,作为RepVL-PAN的权重进行高效的部署(推理) 通过在大规模数据集上...
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 + (...
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。 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._setup_train(w...
3.1 YOLO world的zero-shot能力 下表展现了YOLO-world在LVIS数据集上的zero-shot能力,可见效果优于当前Sota,但速度更快(评估硬件:NVIDIA V100 GPU w/o TensorRT)。 3.2 预训练数据集对效果的影响 用Object365和GlodG就能达到较好的效果。加入CC3M效果提升不是很大,可能是因为CC3M的标签是用2.3.1节的方法生成的...
在预训练阶段,采用AdamW优化器,初始学习率为0.002,权重衰减为0.05。在32个NVIDIA V100 GPU上进行预训练,batch size大小为512。数据增强包括颜色增强、随机仿射、随机翻转和mosaic。文本编码器在预训练时被冻结。 6.2 预训练 简要总结: (1)YOLO-World在Objects365、GQA、Flickr、CC3M数据集上进行预训练。
yolo-world 源码解析(五) .\YOLO-World\yolo_world\datasets\transformers\mm_transforms.py # 导入所需的库importjsonimportrandomfromtypingimportTupleimportnumpyasnpfrommmyolo.registryimportTRANSFORMS# 注册 RandomLoadText 类为 TRANSFORMS 模块@TRANSFORMS.register_module()classRandomLoadText:def__init__(self,...
False, log_imgs=16, multi_scale=False, name='exp', noautoanchor=False, nosave=False, notest=False, project='runs/train', rect=False, resume=False, save_dir='runs\\train\\exp', single_cls=False, sync_bn=False, total_batch_size=16, weights='yolov5s.pt', workers=8, world_size=...
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...
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=dict(type='YOLOWDet...
YOLOv5 has been designed to be super easy to get started and simple to learn. We prioritize real-world results. YOLOv5-P5 640 Figure Figure Notes COCO AP valdenotes mAP@0.5:0.95 metric measured on the 5000-imageCOCO val2017dataset over various inference sizes from 256 to 1536. ...