[WIP]fix doc #5732
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cuicheng01 reviewed Jun 27, 2023
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1.comment需要修复下
2.PULC的9个模型需要加到这个里边
| <td>HRNet_W44_C</td> | ||
| <td><a href="https://paperswithcode.com/method/hrnet">Deep High-Resolution Representation Learning for Visual Recognition</a></td> | ||
| <td><a href="https://arxiv.org/abs/1908.07919">Deep High-Resolution Representation Learning for Visual Recognition</a></td> | ||
| <td><details><summary>Abstract</summary><div>High-resolution representations are essential for position-sensitive vision problems, such as human pose estimation, semantic segmentation, and object detection. Existing state-of-the-art frameworks first encode the input image as a low-resolution representation through a subnetwork that is formed by connecting high-to-low resolution convolutions \emph{in series} (e.g., ResNet, VGGNet), and then recover the high-resolution representation from the encoded low-resolution representation. Instead, our proposed network, named as High-Resolution Network (HRNet), maintains high-resolution representations through the whole process. There are two key characteristics: (i) Connect the high-to-low resolution convolution streams \emph{in parallel}; (ii) Repeatedly exchange the information across resolutions. The benefit is that the resulting representation is semantically richer and spatially more precise. We show the superiority of the proposed HRNet in a wide range of applications, including human pose estimation, semantic segmentation, and object detection, suggesting that the HRNet is a stronger backbone for computer vision problems. All the codes are available at~{\url{this https URL}}. </div></details></td> |
docs/official/README.md Outdated
| <td>MobileNetV3_large_x1_</br>0-FPGM</td> | ||
| <td><a href="https://paperswithcode.com/paper/searching-for-mobilenetv4">Searching for MobileNetV4</a></td> | ||
| <td><a href="https://arxiv.org/abs/1905.02244">Searching for MobileNetV4</a></td> |
docs/official/README.md Outdated
| <td>MobileNetV3_large_x1_</br>0_PACT</td> | ||
| <td><a href="https://paperswithcode.com/paper/searching-for-mobilenetv5">Searching for MobileNetV5</a></td> | ||
| <td><a href="https://arxiv.org/abs/1905.02244">Searching for MobileNetV5</a></td> |
docs/official/README.md Outdated
| <td>MobileNetV3_large_x1_</br>0_KL</td> | ||
| <td><a href="https://paperswithcode.com/paper/searching-for-mobilenetv6">Searching for MobileNetV6</a></td> | ||
| <td><a href="https://arxiv.org/abs/1905.02244">Searching for MobileNetV6</a></td> |
docs/official/README.md Outdated
| <td>CSPDarkNet53</td> | ||
| <td><a href="https://paperswithcode.com/model/csp-resnet?variant=cspresnet50">CSPNet: A New Backbone that can Enhance Learning Capability of CNN</a></td> | ||
| <td><a href="https://arxiv.org/abs/1911.11929">CSPNet: A New Backbone that can Enhance Learning Capability of CNN</a></td> | ||
| <td><details><summary>Abstract</summary><div>Neural networks have enabled state-of-the-art approaches to achieve incredible results on computer vision tasks such as object detection. However, such success greatly relies on costly computation resources, which hinders people with cheap devices from appreciating the advanced technology. In this paper, we propose Cross Stage Partial Network (CSPNet) to mitigate the problem that previous works require heavy inference computations from the network architecture perspective. We attribute the problem to the duplicate gradient information within network optimization. The proposed networks respect the variability of the gradients by integrating feature maps from the beginning and the end of a network stage, which, in our experiments, reduces computations by 20% with equivalent or even superior accuracy on the ImageNet dataset, and significantly outperforms state-of-the-art approaches in terms of AP50 on the MS COCO object detection dataset. The CSPNet is easy to implement and general enough to cope with architectures based on Re |
| <td>CSPDarkNet53</td> | ||
| <td><a href="https://paperswithcode.com/model/csp-resnet?variant=cspresnet50">CSPNet: A New Backbone that can Enhance Learning Capability of CNN</a></td> | ||
| <td><a href="https://arxiv.org/abs/1911.11929">CSPNet: A New Backbone that can Enhance Learning Capability of CNN</a></td> | ||
| <td><details><summary>Abstract</summary><div>Neural networks have enabled state-of-the-art approaches to achieve incredible results on computer vision tasks such as object detection. However, such success greatly relies on costly computation resources, which hinders people with cheap devices from appreciating the advanced technology. In this paper, we propose Cross Stage Partial Network (CSPNet) to mitigate the problem that previous works require heavy inference computations from the network architecture perspective. We attribute the problem to the duplicate gradient information within network optimization. The proposed networks respect the variability of the gradients by integrating feature maps from the beginning and the end of a network stage, which, in our experiments, reduces computations by 20% with equivalent or even superior accuracy on the ImageNet dataset, and significantly outperforms state-of-the-art approaches in terms of AP50 on the MS COCO object detection dataset. The CSPNet is easy to implement and general enough to cope with architectures based on Re |
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这里的指标格式和下边的没有对齐，可以可视化看下
| <td>CSPDarkNet53</td> | ||
| <td><a href="https://paperswithcode.com/model/csp-resnet?variant=cspresnet50">CSPNet: A New Backbone that can Enhance Learning Capability of CNN</a></td> | ||
| <td><a href="https://arxiv.org/abs/1911.11929">CSPNet: A New Backbone that can Enhance Learning Capability of CNN</a></td> | ||
| <td><details><summary>Abstract</summary><div>Neural networks have enabled state-of-the-art approaches to achieve incredible results on computer vision tasks such as object detection. However, such success greatly relies on costly computation resources, which hinders people with cheap devices from appreciating the advanced technology. In this paper, we propose Cross Stage Partial Network (CSPNet) to mitigate the problem that previous works require heavy inference computations from the network architecture perspective. We attribute the problem to the duplicate gradient information within network optimization. The proposed networks respect the variability of the gradients by integrating feature maps from the beginning and the end of a network stage, which, in our experiments, reduces computations by 20% with equivalent or even superior accuracy on the ImageNet dataset, and significantly outperforms state-of-the-art approaches in terms of AP50 on the MS COCO object detection dataset. The CSPNet is easy to implement and general enough to cope with architectures based on Re |
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这里的softmax_triplet建议改成 ReID_softmax_triplet
| <td>CSPDarkNet53</td> | ||
| <td><a href="https://paperswithcode.com/model/csp-resnet?variant=cspresnet50">CSPNet: A New Backbone that can Enhance Learning Capability of CNN</a></td> | ||
| <td><a href="https://arxiv.org/abs/1911.11929">CSPNet: A New Backbone that can Enhance Learning Capability of CNN</a></td> | ||
| <td><details><summary>Abstract</summary><div>Neural networks have enabled state-of-the-art approaches to achieve incredible results on computer vision tasks such as object detection. However, such success greatly relies on costly computation resources, which hinders people with cheap devices from appreciating the advanced technology. In this paper, we propose Cross Stage Partial Network (CSPNet) to mitigate the problem that previous works require heavy inference computations from the network architecture perspective. We attribute the problem to the duplicate gradient information within network optimization. The proposed networks respect the variability of the gradients by integrating feature maps from the beginning and the end of a network stage, which, in our experiments, reduces computations by 20% with equivalent or even superior accuracy on the ImageNet dataset, and significantly outperforms state-of-the-art approaches in terms of AP50 on the MS COCO object detection dataset. The CSPNet is easy to implement and general enough to cope with architectures based on Re |
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这里的softmax_triplet_with_center建议改成 ReID_softmax_triplet_with_center
| <td>CSPDarkNet53</td> | ||
| <td><a href="https://paperswithcode.com/model/csp-resnet?variant=cspresnet50">CSPNet: A New Backbone that can Enhance Learning Capability of CNN</a></td> | ||
| <td><a href="https://arxiv.org/abs/1911.11929">CSPNet: A New Backbone that can Enhance Learning Capability of CNN</a></td> | ||
| <td><details><summary>Abstract</summary><div>Neural networks have enabled state-of-the-art approaches to achieve incredible results on computer vision tasks such as object detection. However, such success greatly relies on costly computation resources, which hinders people with cheap devices from appreciating the advanced technology. In this paper, we propose Cross Stage Partial Network (CSPNet) to mitigate the problem that previous works require heavy inference computations from the network architecture perspective. We attribute the problem to the duplicate gradient information within network optimization. The proposed networks respect the variability of the gradients by integrating feature maps from the beginning and the end of a network stage, which, in our experiments, reduces computations by 20% with equivalent or even superior accuracy on the ImageNet dataset, and significantly outperforms state-of-the-art approaches in terms of AP50 on the MS COCO object detection dataset. The CSPNet is easy to implement and general enough to cope with architectures based on Re |
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PeleeNet的写法不对，另外需要说明是哪个模型
| <td>CSPDarkNet53</td> | ||
| <td><a href="https://paperswithcode.com/model/csp-resnet?variant=cspresnet50">CSPNet: A New Backbone that can Enhance Learning Capability of CNN</a></td> | ||
| <td><a href="https://arxiv.org/abs/1911.11929">CSPNet: A New Backbone that can Enhance Learning Capability of CNN</a></td> | ||
| <td><details><summary>Abstract</summary><div>Neural networks have enabled state-of-the-art approaches to achieve incredible results on computer vision tasks such as object detection. However, such success greatly relies on costly computation resources, which hinders people with cheap devices from appreciating the advanced technology. In this paper, we propose Cross Stage Partial Network (CSPNet) to mitigate the problem that previous works require heavy inference computations from the network architecture perspective. We attribute the problem to the duplicate gradient information within network optimization. The proposed networks respect the variability of the gradients by integrating feature maps from the beginning and the end of a network stage, which, in our experiments, reduces computations by 20% with equivalent or even superior accuracy on the ImageNet dataset, and significantly outperforms state-of-the-art approaches in terms of AP50 on the MS COCO object detection dataset. The CSPNet is easy to implement and general enough to cope with architectures based on Re |
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下边所有的模型和之前的指标格式没有对上，可以可视化验证看下
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