然后,gt_bboxes、gt_labels分开,并得到相应的gt_mask(用于区分正负样本) 3. 正负样本分配 大体流程: (1) 网络输出的pred_scores[bx8400xcls_num],进行sigmoid处理(每个类别按照2分类处理)。 (2) 经过解码的pred_bboxes[bx8400x4], 与 stride_tensor[8400x1]相乘,将bboxes转换到网络输入尺度[bx3x640x640...
the model processes each example in the training set once and updates its parameters based on the learning algorithm. Multiple epochs are usually needed to allow the model to learn and refine its parameters over time.
we replace the original heavyweight ViT-H encoder (632M) with a smaller Tiny-ViT (5M). On a single GPU, MobileSAM operates at about 12ms per image: 8ms on the image encoder and 4ms on the mask decoder.
yolov8_n_mask-refine_syncbn_fast_8xb16-500e_coco.py yolov8_n_syncbn_fast_8xb16-500e_coco.py yolov8_s_fast_1xb12-40e_cat.py yolov8_s_mask-refine_syncbn_fast_8xb16-500e_coco.py yolov8_s_syncbn_fast_8xb16-500e_coco.py ...
| YOLOv8-n | P5 | 640 | Yes | Yes | Yes | 2.5 | 37.4 (+0.2) | [config](../yolov8/yolov8_n_mask-refine_syncbn_fast_8xb16-500e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov8/yolov8_n_mask-refine_syncbn_fast_8xb16-500e_coco/yolov8_n_mask-refine_...
Object detection methods can be mainly divided into two-stage detectors19, such as Faster R-CNN20, Mask R-CNN21 and Cascade R-CNN22, and single-stage detectors23, such as YOLO10,11,12 and RetinaNet24. Two-stage detectors first generate candidate regions and then classify and refine the bo...
Mask R-CNN: Mask Region-based Convolutional Neural Network CA: Coordinate Attention AP: Average Precision SimAM: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks EMA: Efficient Multi-Scale Attention SE: Squeeze and Excitation Grad-CAM: Gradient-weighted Class Activation...
In comparison, two-shot detection methods, such as Mask R-CNN, operate in a more complex process. The initial pass generates a set of proposals for potential object locations, and a subsequent pass refines these proposals to make final predictions Jin et al. (2020); Yang et al. (2021)....
head.n_strips predictions = torch.concat([cls_logits, anchor_params, reg[:, :, 3:4], reg_xs], dim=2) # predictions = torch.concat([cls_logits, anchor_params_, reg[:, :, 3:4], reg_xs], dim=2) predictions_list.append(predictions) if stage != self.head.refine_layers - 1: ...
Grid Mask Auto Augment Random Perspective 模型性能概览 云端模型性能对比 各模型结构和骨干网络的代表模型在COCO数据集上精度mAP和单卡Tesla V100上预测速度(FPS)对比图。 说明: PP-YOLOE是对PP-YOLO v2模型的进一步优化,在COCO数据集精度51.6%,Tesla V100预测速度78.1FPS ...