MMDetction之配置文件:Faster RCNN resnet50 without FPN

官方所提供的配置文件都是带有FPN结构的,所以上传一个不带FPN结构的配置文件,实验结果还在测试。

# faster rcnn caffe dc5.py
# model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
    type='FasterRCNN',
    data_preprocessor=dict(
        type='DetDataPreprocessor',
        mean=[103.530, 116.280, 123.675],
        std=[1.0, 1.0, 1.0],
        bgr_to_rgb=False,
        pad_size_divisor=32),
    backbone=dict(
        type='ResNet',
        depth=50,
        num_stages=4,
        strides=(1, 2, 2, 1),
        dilations=(1, 1, 1, 2),
        out_indices=(3, ),
        frozen_stages=1,
        norm_cfg=norm_cfg,
        norm_eval=True,
        style='caffe',
        init_cfg=dict(
            type='Pretrained',
            checkpoint='open-mmlab://detectron2/resnet50_caffe')),
    rpn_head=dict(
        type='RPNHead',
        in_channels=204
### C2F Faster Technology Information C2F Faster appears to reference a specific technology or project, but direct details about "C2F Faster" are not provided within the given references[^1][^2][^3]. However, based on common naming conventions and industry trends, several interpretations could apply: #### Possible Interpretations of C2F Faster In many contexts involving optimization and performance enhancement, terms like “faster” often relate to improvements in speed or efficiency. For instance, when discussing machine learning models or algorithms, one might encounter names indicating faster processing capabilities. If considering an algorithmic context, particularly related to computer vision tasks such as object detection, there exists a well-known model called **Faster R-CNN**, which stands for Region-based Convolutional Neural Network. This model significantly improves upon its predecessors by accelerating region proposal generation through shared convolutional features across the entire image. However, without explicit documentation linking directly to "C2F," it remains speculative whether any connection exists between these concepts. If "C2F" refers specifically to cloud-to-fog computing environments, then "C2F Faster" could imply optimizations aimed at enhancing data transfer speeds from centralized clouds down to edge devices located closer to end-users. For precise information regarding "C2F Faster," consulting official publications or technical documents associated with the originating organization would provide more accurate insights into this particular term's meaning and applications. ```python # Example code snippet demonstrating usage of Faster R-CNN framework import torch from torchvision.models.detection import fasterrcnn_resnet50_fpn model = fasterrcnn_resnet50_fpn(pretrained=True) model.eval() ```
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