Efficientnet vs efficientdet. 09070v1 Quick Overview of the Paper.

• Efficientnet vs efficientdet Faster R-CNN Provide your own image below to test YOLOv8 and YOLOv9 model checkpoints trained on the Microsoft COCO dataset. Performance of EfficientDet-D3 (EfficientNet-B3), RetinaNet (ResNeSt101-RPN), Faster RCNN (ResNeSt101-RPN), YOLOv4 (CSPDarknet-53), Faster RCNN (Ours) and RetinaNet (Ours). Depth (d): Scaling network depth is the most commonly used method. Feb 26, 2025 · EfficientDet utilizes an EfficientNet backbone, a Bi-directional Feature Pyramid Network for effective multi-scale feature fusion, and a compound scaling method that uniformly scales the resolution, depth, and width for all backbone, feature network, and box/class prediction networks. Apart from just explaining the key ideas of the paper, I also tried replicating some of their results on a small ImageNet-like dataset ( ImageNette , for those of you familiar). YOLOv8 offers a superior balance of accuracy and speed, especially for deployment scenarios involving GPUs where real-time performance is critical. Finally, we also observe that the recently introduced EfficientNets [] achieve better efficiency than previous commonly used backbones. 3%). MBConv Layer (Inverted Bottleneck): The key idea in a Bottleneck layer is to first use a 1x1 convolution to bring down the number of channels and apply the 3x3 or 5x5 convolution operation to the reduced number Jun 3, 2021 · 이전글 : [AI/Self-Study] - EfficientNet 모델 구조 EfficientNet 모델 구조 EfficientNet - B0 baseline 네트워크 구조 EfficientNet B0 전체 모델 구조 파악 MBConv1 Block 구조 (= mobile inverted bottleneck convolution) MobileNetV2 and MobileNetV3 Depthwise Separable Convoluti. Jul 12, 2024 · EfficientDet uses EfficientNet as its backbone network. It has an EfficientNet backbone and a custom detection and classification network. This allows EfficientDet to converge more quickly to a better solution, resulting in a lower final loss value. В последние годы был достигнут огромный Compare EfficientNet vs. In this article, we have explored EfficientDet model architecture which is a modification of EfficientNet model and is used for Object Detection application. com 1. Mar 20, 2022 · Starting from this baseline architecture, the authors scaled the EfficientNet-B0 using Compound Scaling to obtain EfficientNet B1-B7. Feb 25, 2025 · While EfficientDet shows very fast CPU speeds, YOLOv8 achieves higher mAP across comparable model sizes (e. We analyze their architectures, performance benchmarks, and suitability for different applications to assist you in selecting the optimal model for your EfficientDet由 EfficientNet Backbone, BiFPN layer, Class 和 Box prediction net 四部分组成. In particular, our EfficientNet-B7 achieves new state-of-the-art 84. Dec 3, 2020 · The EfficientDet authors use search to find an optimal scaling threshold from EfficientDet-D0 to EfficientDet-D1, and use this setting to linearly scale up to the famed EfficientDet-D7. It focuses on achieving high accuracy and efficiency through a scalable architecture. The existing object detection models do not work well underwater. It employs compound scaling to uniformly scale the model's depth, width, and resolution, offering a family of models (D0-D7) with varying trade-offs between accuracy and Feb 26, 2025 · EfficientDet vs YOLOv8; EfficientDet vs YOLOv5; YOLOv7 vs YOLOv8; YOLOv7 vs YOLOv5; RT-DETR vs YOLOv7; YOLOX vs YOLOv7; Explore the latest models like YOLOv10 and YOLO11. A key feature is the BiFPN (Bi-directional Feature Pyramid Network) , a weighted feature fusion mechanism designed for efficient multi-scale feature aggregation. This is a paper in 2020 Feb 26, 2025 · EfficientDet utilizes the EfficientNet backbone, known for its compound scaling method that balances network depth, width, and resolution. Le from Google Research. Their model runs 5x faster on CPU Jul 27, 2021 · Alex Shonenkov has a clear and concise Kaggle kernel that illustrates fine-tuning EfficientDet to detecting wheat heads using EfficientDet-PyTorch; it appears to be the starting point for most 本日は、 EfficientNet-Lite （GitHub、TFHub）についてお知らせします。EfficientNet-Lite は、モバイルの CPU や GPU、そして EdgeTPU で動作するように設計されており、TensorFlow Lite を使って実行します。EfficientNet-Lite は、EfficientNet のパワーをエッジデバイスに提供します。 Compare EfficientNet vs. Literature Review: Research on AMVs is ongoing and focuses on enabling autonomous vessels to operate in different marine environments, perform various tasks, and serve different applications. On the COCO dataset for image detection, EfficientDet is shown to have the best performance among peer models relative to model size. With EfficientNet-B3 as the backbone, it already increased accuracy by 3%; It achieved 55. This page provides a detailed technical comparison between two popular models: Ultralytics YOLOv5 and EfficientDet. By contrast, the five year-old ResNet produced very good results on every single dataset that I came across, which is truly impressive! 4 days ago · When comparing EfficientNet and MobileNet, several performance metrics can be considered: Accuracy: EfficientNet generally achieves higher accuracy on standard datasets like ImageNet, while MobileNet is optimized for lower latency. . A key innovation is compound scaling, which uniformly scales the backbone, BiFPN, and detection head resolution/depth/width to create a family of models (D0-D7) balancing accuracy and efficiency. SOTA (State-of-the-Art) Performance : EfficientNet models achieve higher accuracy with fewer parameters compared to ResNet and MobileNet. We will analyze their architectures, performance metrics, and Feb 25, 2025 · EfficientDet utilizes the efficient EfficientNet backbone combined with a novel Bi-directional Feature Pyramid Network (BiFPN) for effective multi-scale feature fusion. YOLOv8 moves like a butterfly, delivering real-time performance that makes EfficientDet look This one is about the EfficientNet architecture and the compound scaling algorithm that was introduced in the EfficientNet paper. We train from the EfficientNet base backbone, without using a pre-trained checkpoint for the detector portion of the network. EfficientNet is a family of convolutional neural networks that scale up effectively using a compound scaling method. ) COCO contains 80 object classes that span a range of vision semantics and is considered Feb 25, 2025 · EfficientDet utilizes an EfficientNet backbone combined with a BiFPN (Bi-directional Feature Pyramid Network) neck for feature fusion. YOLOv7 Provide your own image below to test YOLOv8 and YOLOv9 model checkpoints trained on the Microsoft COCO dataset. The compound scaling method is justified by the intuition that if the input image is bigger, then the network needs more layers to increase the receptive field and more channels to capture more fine-grained patterns on May 23, 2020 · 下圖為EfficientDet的模型架構，使用ImageNet預訓練的EfficientNet作為backbone，BiFPN作為feature network。 BiFPN從backbone取得特徵，接著自上而下和自下而上進行雙向特徵融合，接著這些融合的特徵被送到Class/Box prediction network，分別生成類別和BBOX預測。 Apr 22, 2020 · EfficientNet: Motivation and Design. Recently Jun 1, 2020 · EfficientDet is a family of single-stage object detection models based on the EfficientNet backbone. BiFPN을 Feature Network로 사용하였고, level 3-7 feature에 적용을 하였습니다. This page provides a detailed technical comparison between YOLOv9 and EfficientDet, two state-of-the-art models renowned for their performance and efficiency in Nov 3, 2020 · The Google AutoML implementation of EfficientDet supports a variety of hyperparameter settings that allow you to easily fine-tune the model. EfficientNet is the backbone architecture used in the model. Feb 3, 2021 · EfficientDet 是 Google 基於 EfficientNet 擴展出的目標檢測模型發表於 CVPR 2020，主要改進點為提出新的特徵融合方法 BiFPN、對於檢測模型的複合縮放 Compound Nov 27, 2024 · 论文链接： EfficientDet、EfficientNet、EfficientNetV2. 4% top-1 / 97. 2ms vs 128. BiFPN network: EfficientNetは、画像分類タスクで優れたパフォーマンスを示すモデルであり、EfficientDetにもその高性能が活かされています。 EfficientDetの実装. First, we propose a weighted bi-directional feature pyramid network (BiFPN), which allows easy and fast multi-scale feature fusion; Second, we propose a compound Jan 22, 2021 · MobileNets, EfficientNet and EfficientDet. EfficientDet: A Detailed Comparison for Object Detection. EfficientDet: A Detailed Comparison Choosing the optimal object detection model is critical for computer vision tasks, balancing accuracy, speed, and computational resources. lynnshin. Impressive performance! The question is: Does it generalize to other datasets? In some of my projects (retail images), EfficientNet did not give good results, so it's hard to believe EfficientDet will do. The pretrained EfficientNet weights on imagenet are downloaded from Callidior/keras-applications/releases; The pretrained EfficientDet weights on coco are converted from the official release google/automl. We train for 20 epochs across our training set. 虽然YOLO 和 EfficientDet 在特定领域具有很强的性能，但在 Ultralytics YOLO生态系统中的模型，如 YOLOv8和最新的 YOLO11提供了令人信服的替代方案，通常在整体平衡性和可用性方面表现出色。 Camera trap image analysis, although critical for habitat and species conservation, is often a manual, time-consuming, and expensive task. EfficientNet Provide your own image below to test YOLOv8 and YOLOv9 model checkpoints trained on the Microsoft COCO dataset. PythonとTensorFlowを使用したEfficientDetの基本的な実装は以下のようになります。 Jan 5, 2023 · For this reason we will consider the ResNet34 and ResNet50 models of the ResNet family [1], the EfficientNet-B4 and EfficientNet-B5 models of the EfficientNet family [2], the size s and m of the EfficientDet architecture employs EfficientNet as the backbone network, BiFPN as the feature network, and shared class/box prediction network. 4 vs 53. EfficientDet-Lite has 5 different versions: Lite0 to Lite4. EfficientNet is also written by the same authors at Google. There are multiple ways to achieve the trade-off between model efficiency and model accuracy such as lite network design, parameter quantization, model compression and knowledge distillation. Image Source: Google AI Blog. 또한 top-down, bottom-up bidirectional feature fusion을 반복적으로 사용하였습니다. EfficientDet uses ImageNet pre-trained EfficientNet architecture as a backbone. COCO can detect 80 common objects, including cats, cell phones, and cars. Sep 14, 2022 · EfficientNet-B0 is used on EfficientDet-D0 [2]. In this paper, we systematically study neural network architecture design choices for object detection and propose several key optimizations to improve efficiency. EfficientDet is an improvement upon EfficientNet, so we’ll May 2, 2023 · EfficientDet. This paper improves the structure of EfficientDet detector and proposes the EfficientDet-Revised May 29, 2019 · Model Size vs. Jan 22, 2021. Compare MobileNet V2 Classification vs. Le / CVPR 2020) EfficientDet은 EfficientNet을 기반으로 설계된 Object Detection 네트워크로, 모델의 효율성에 가장 큰 초점을 두었다. RTDETRv2 models generally achieve higher mAP scores, showcasing the accuracy benefits of transformers, and maintain good GPU speeds, though often requiring more parameters and FLOPs. 1. Experiments on COCO show that EfficientDet outperforms prior models across different resource constraints in terms of accuracy and efficiency for object detection and semantic segmentation tasks. Apr 2, 2021 · EfficientNet是2019年的一篇文章，它针对FLOPs与参数量采用NAS搜索得到EfficientNet-B0，然后通过复合尺度缩放得到了更大版本的模型，比如EfficientNetB1-B7。 上表给出了EfficientNet与其他方法在精度、参数量以及Flops方面的对比。 Nov 1, 2024 · 最近efficientnet和efficientdet在分类和检测方向达到了很好的效果，他们都是根据Google之前的工作，mobilenet利用nas搜索出来的结构。 之前也写过《轻量级 深度学习 网络 概览》，里面提到过 mobilenet v1和 mobilenet v2的一些思想。 Jan 1, 2024 · Deep learning techniques have led to an increased use of Convolutional Neural Networks (CNN) in recognizing images for marine surveys and classifying underwater objects. Hence, whenever you are facing an object detection problem, you could take EfficientDet into consideration. Nov 20, 2019 · What is EfficientDet? EfficientDet is a state-of-the-art object detection model for real-time object detection originally written in Tensorflow and Keras but now having implementations in PyTorch--this notebook uses the PyTorch implementation of EfficientDet. Based o n previous research, further research is needed to validate the hypotheses from previous re search es and determine the effect of . Apr 14, 2021 · EfficientDet architecture: It employs EfficientNet as the backbone network, BiFPN as the feature network and shared class/box prediction network. The following Jan 18, 2024 · Speed vs. , YOLOv8m vs EfficientDet-d5). P7 P6 P5 P4 P3 (a) FPN (b) PANet (c) NAS-FPN (d) BiFPN P7 P6 P5 P 4 P3 P7 P6 P5 4 P P7 P P5 P4 P3 repeated blocks repeated blocks Figure 2: Feature network design – (a) FPN [20] introduces a top-down pathway to fuse multi-scale features from level 3 to EfficientDet 模型，尤其是较小的模型，显示出出色的CPU 速度和较低的资源需求（参数和 FLOPs）。RTDETRv2 模型通常能获得更高的 mAP 分数，展示了变压器的精度优势，并能保持良好的GPU 速度，尽管通常需要更多参数和 FLOP。 Mar 18, 2024 · Model size vs accuracy demonstrating EfficientNet performance –Source Model scaling can be achieved in three ways: by increasing model depth, width, or image resolution. EfficientDet architecture follows the one-stage Feb 17, 2025 · YOLOv9 vs. Source: arXiv:1911. Recently, the Google Brain team released their own ConvNet model called EfficientNet. Thus, automating this process would allow large-scale Compare YOLOv11 vs. Combining EfficientNet backbones with our propose BiFPN and compound scaling, we have developed a new family of object detectors, named EfficientDet, which consistently achieve better accuracy with much fewer parameters and FLOPs than previous object detectors. All accuracy numbers are for single-model single-scale without ensemble or test-time augmentation. EfficientDet, developed by the Google Brain team, was introduced in 2019. Compared with the widely used ResNet-50, the EfficientNet-B4 used similar FLOPS, while improving the top-1 accuracy from 76. As shown in the table below, larger models achieve higher mAP scores but come with increased latency and computational cost. The main idea behind EfficientDet is to use efficient backbone networks, such as EfficientNet, along with a novel compound scaling method to balance model size and accuracy. The compound scaling Paper : EfficientDet: Scalable and Efficient Object Detection (Mingxing Tan, Ruoming Pang, Quoc V. EfficientDet의 backbone으로는 ImageNet-pretrained EfficientNet을 사용하였습니다. (See our prior post for a comprehensive breakdown EfficientDet. Apr 25, 2020 · 文 / 软件工程师 Renjie Liu 2019 年 5 月，Google 发布了一系列名为 EfficientNet 的图像分类模型，此类模型在降低一个数量级的参数和算力消耗的情况下，实现了最前沿 (SOTA) 的精度。如果 EfficientNet 可以在边缘设备上运行，则将为计算资源受限的移动和 IoT 设备开拓全新的应用场景。 今天，我们很高兴宣布 YOLOv8 and EfficientDet offer enhanced accuracy, reduced complexity, scalability, robustness, and generalization for ship detection. Mar 22, 2025 · Variations from B0 to B7: EfficientNet-B0 (smallest) to EfficientNet-B7 (largest) provide trade-offs between speed and accuracy. Feb 26, 2025 · YOLOv5 vs. 选择正确的物体检测模型对于计算机视觉项目至关重要。本页将对EfficientDet和YOLOv10 这两种具有不同优势的流行模型进行技术比较。 Mar 21, 2021 · EfficientNet based Models (EfficientDet) provide the best overall performance (MAP of 51. Mar 1, 2024 · EfficientDet is a state-of-the-art object detection model that combines efficiency and accuracy. 在说EfficientDet之前得先了解EfficientNet架构。 在EfficientNet没出来之前，基本就是ResNet的天下。看一下官方给的对比。左边是第一代的和当时其它主流模型的对比，右边是第二代的跟自己 Jan 5, 2020 · EfficientDet also uses a compound scaling method to jointly scale the backbone network, BiFPN, prediction network, and input resolution. 66ms vs 10. 主干网中的3-7级{P3, P4, P5, P6, P7}特征会传递给特征网络, 并反复应用BiFPN（自上而下＆自下而上）的双向特征融合. 7) than EfficientDet-d2 (43. Compare EfficientNet vs. The Startup · 5 min read · Nov 23, 2020--Listen. While the total number of objects detected is only slightly higher than EfficientDet-Lite-D0, it generally had 90% - 99% confidence in its predictions, while the EfficientDet model confidences ranged widely between 50% - 95%. Model Size: EfficientNet models tend to be larger in size compared to MobileNet, which is designed to be compact. 4x smaller than the best existing CNN. 0) while being significantly faster (2. YOLO Models. This indicates that EfficientNet is not only more accurate but also more computationally efficient than existing CNNs Feb 22, 2024 · ### EfficientNet与EfficientDet的关系 EfficientDet是谷歌在EfficientNet基础上提出的一种高效目标检测器，其网络结构结合了EfficientNet的高效性和目标检测器的特点，实现了在保持准确性的情况下提高了检测速度。 Apr 7, 2024 · EfficientDet models, especially smaller ones, demonstrate excellent CPU speed and lower resource requirements (params, FLOPs). Introduction Tremendous progresses have been made in recent years towards more accurate object detection; meanwhile, state-of-the-art object detectors also become increasingly more expensive. Sep 27, 2023 · EfficientDet 影响网络的性能(或者说规模)的三大因素：depth(layer的重复次数), width(特征图channels), resolution(特征图宽高)。 EfficientDet是以EfficientNet作为BackBone提取特征，以BiFPN作为加强特征提取网络。依 Feb 26, 2025 · YOLO11 vs EfficientDet: A Detailed Technical Comparison This page offers a detailed technical comparison between Ultralytics YOLO11 and EfficientDet, two prominent object detection models. 官方链接：EfficientDet. YOLOv5 Provide your own image below to test YOLOv8 and YOLOv9 model checkpoints trained on the Microsoft COCO dataset. Share. This fight hinges on one crucial clash: speed versus accuracy. Dec 10, 2022 · It’s able to successfully detect 306 out of the 335 total objects in the test images. It was proposed by researchers at Google in 2019. Le. AP val is for validation accuracy, all other AP results in the table are for COCO test-dev2017. Accuracy Comparison. Nov 28, 2019 · EfficientDet: A new family of Backbone network: Same width/depth scaling coefficients of EfficientNet-B0 to B6 are used so that ImageNet-pretrained checkpoints can be used. May 1, 2020 · EfficientDet is performant, in both speed and accuracy . g. EfficientNet. The first column is the Feb 26, 2025 · EfficientDet, developed by Google, is a family of object detection models focused on achieving state-of-the-art accuracy with remarkable efficiency through architectural innovations like BiFPN and the use of EfficientNet backbones. These configurations were designed for mobile applications. 1 AP(average precision) on COCO test-dev that contains 77 million parameters. For example, the latest AmoebaNet-based NAS-FPN detector [45] requires 167M parameters and 3045B Feb 25, 2025 · Ultralytics Advantage and Alternatives. Feb 26, 2025 · EfficientDet Overview. val denotes validation results, test-dev denotes test-dev2017 results. Conclusion. EfficientDet excels in speed, especially the smaller models (D0-D3) on both CPU and GPU, and has significantly lower parameter counts and FLOPs, making it more resource-friendly. EfficientDet和SSD, RetinaNet一样都属于single-shot detectors . Model efficiency has become increasingly important in computer vision. Compare Faster R-CNN vs. The YOLO family of models, written in the Darknet framework, has a rich history starting with Joseph Redmon (Github moniker pjreddie). Compare Detectron2 vs. Choosing the right object detection model is crucial for successful computer vision applications. EfficientNet uses a compound coefficient $\phi$ to uniformly scales network width, depth, and resolution in a principled way. May 13, 2020 · EfficientDet Model architecture. tistory. Nov 23, 2020 · EfficientNet and EfficientDet Explained. Published in. 2M vs 8. Accuracy: The Main Event. 07ms) and roughly half the FLOPs. Nov 22, 2019 · EfficientDet Architecture. Hope this tutorial has offered enough help for you to get EfficientDet working on your own dataset. Avinash · Follow. Nov 3, 2023 · Model architecture: EfficientDet’s architecture is based on the EfficientNet backbone, resulting in an efficient and scalable model that learns with fewer parameters than YOLO v8. Aug 25, 2021 · One of its configurations is the tf_lite0, derived from tf_efficientdet_lite0, in turn derived from efficientnet/lite0. EfficientDet (pytorch implementation) is a family of object detection models that use EfficientNet as the backbone network. May 12, 2020 · 来自 Google Brain 的 EfficientNet 和 EfficientDet 为图像分类和检测构造了一个优质 Baseline 网络，并提出了一种放大网络的方法以速度换取更高的精度。 Ultralytics 的优势和替代品. Jul 20, 2023 · EfficientDet paper has mentioned its 7 family members. It is known for its high accuracy and efficiency, making Apr 15, 2020 · Building upon our previous work on scaling neural networks (EfficientNet), and incorporating a novel bi-directional feature network (BiFPN) and new scaling rules, EfficientDet achieves state-of-the-art accuracy while being up to 9x smaller and using significantly less computation compared to prior state-of-the-art detectors. This article gives a short summary from the point of neural architecture design. 6% (+6. MobileNet SSD v2 Provide your own image below to test YOLOv8 and YOLOv9 model checkpoints trained on the Microsoft COCO dataset. 3% of ResNet-50 to 82. Mar 14, 2020 · EfficientDetは、バックボーンのネットワークとしてEfficientNetを使用し、そこから得られた複数解像度の特徴マップをBiFPNによって混合し、それぞれの解像度で分類・矩形予測のヘッドがついている、という構造になっています。 Feb 26, 2025 · RTDETRv2 generally achieves higher mAP val 50-95 scores, indicating better accuracy, particularly with larger model variants. While DAMO-YOLO and EfficientDet offer strong performance in specific areas, models within the Ultralytics YOLO ecosystem, such as YOLOv8 and the latest YOLO11, provide compelling alternatives often excelling in overall balance and usability. 92ms) and having fewer parameters (7. You can check it out here . 09070v1 Quick Overview of the Paper. It uses a number of optimizations to achieve high performance while maintaining low latency [8 Mar 31, 2023 · Model Size vs. EfficientNet-B0 is the baseline network developed by AutoML MNAS, while Efficient-B1 to B7 are obtained by scaling up the baseline network. The EfficientNet model was proposed in EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks by Mingxing Tan and Quoc V. MobileNetV1 Jul 29, 2021 · Finally, with EfficientNet as backbones, a family of object detectors, EfficientDet, is formed, consistently achieve much better efficiency than prior art, as shown above. Even the largest YOLOv10x model surpasses EfficientDet-d7 in mAP (54. EfficientNet 설명 (2019, 2020) EfficientNet 은 Image Classification EfficientDet is a type of object detection model, which utilizes several optimization and backbone tweaks, such as the use of a BiFPN, and a compound scaling method that uniformly scales the resolution,depth and width for all backbones, feature networks and box/class prediction networks at the same time. EfficientNet forms the backbone of the EfficientDet architecture, so we will cover its design before continuing to the contributions of EfficientDet. Compare MobileNet SSD v2 vs. 1% top-5 accuracy, while being 8. Benchmarking Data Source The data used in the chart above is harvested from the Tensorflow Object detection model zoo[^1]. 7) with drastically lower latency (12. Comparison of EfficientDet detectors[0–6] with other SOTA object detection models. Jun 16, 2021 · We adapted the neural architecture search technique published in the EfficientDet paper, then optimized the model architecture for running on mobile devices and came up with a novel mobile object detection model family called EfficientDet-Lite. Our implementation uses the base version of EfficientDet-d0. 1M). The proposed BiFPN serves as the feature network, which takes level 3–7 features Jun 30, 2023 · 既EfficientNet之后，Tan Mingxing等人再接再厉，在物体检测领域的特征融合和检测头等部分也采用了相似的方法进行研究和搜索，提出了EfficientDet的网络，在COCO数据集上吊打其他方法。EfficientDet主要有两个创新点，一个是FPN的加强版BiFPN，另一个是 Mar 16, 2020 · In May 2019, Google released a family of image classification models called EfficientNet, which achieved state-of-the-art accuracy with an order of magnitude of fewer computations and parameters. This project is an implementation of key components proposed in the research paper, "EfficientDet: Scalable and Efficient Object Detection" by Mingxing Tan, Ruoming Pang, and Quoc V. 2 for EfficientDet D6). If EfficientNet can run on edge, it opens the door for novel applications on mobile and IoT where computational resources are constrained. EfficientNets are a family of image classification models, which achieve state-of-the-art accuracy, yet being an order-of-magnitude smaller and faster than previous models. Table of contents: Introduction to EfficientDet; Components of Object Detection Models; Componets of EfficientDet; Implementation: Inference, Evaluation and Training; Pre-requisite: Object Feb 26, 2025 · EfficientDet models offer a range of performance points, scaling from the lightweight EfficientDet-d0 to the highly accurate EfficientDet-d7. Feb 26, 2025 · For instance, YOLOv10-S achieves a higher mAP (46. master/efficientdet. EfficientDet utilizes EfficientNet backbones and a novel Bi-directional Feature Pyramid Network (BiFPN) for effective feature fusion across different scales. Based on these optimizations and EfficientNet backbones, we have developed a new family of object detectors, called EfficientDet, which consistently achieve much better efficiency than prior art across a wide spectrum of resource constraints. [EfficientDet Architecture] 作者基于EfficientNet, 提出对检测器的backbone等网络进行模型缩放，并且结合提出的BiFPN提出了新的检测器家族，叫做EfficientDet。 本文提出的检测器的主要遵循one-stage的设计思想，通过优化网络结构，可以达到更高的效率和精度。 May 25, 2020 · Привет, Хабр! представляю вашему вниманию разбор статьи «EfficientDet: Scalable and Efficient Object Detection» автора Mingxing Tan, Ruoming Pang, Quoc V. I have not used EfficientNet in practice, but I'd suggest, in reply to your observations, that even if you don't need the very best performance on ImageNet or COCO, there still seem like 3 advantages of EfficientNet: fewer FLOPS/parameters for whatever level of classification performance your application needs; hard to argue with that. EfficientDet excels in scenarios where parameter and FLOP efficiency are paramount, offering scalability across different resource budgets. Compare YOLOv8 vs. These Apr 13, 2020 · For training, we import a PyTorch implementation of EfficientDet courtesy of signatrix. EfficientDet 与 YOLOv10：详细比较. EfficientNet set out to study the scaling process of ConvNet Dec 17, 2020 · Also, the architecture of EfficientDet employs the ImageNet pre-trained EfficientNet as the backbone of the network. Sep 8, 2022 · Intelligent detection of marine organism plays an important part in the marine economy, and it is significant to detect marine organisms quickly and accurately in a complex marine environment for the intelligence of marine equipment. mjjg stlrjzg ewepm kjbew sxyul iisidl jabs rltz erf sgwlj nvrd ieembf uybtn lbwdt erdln