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faster r-cnn (vgg16)  (Xilinx Inc)

 
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    Structured Review

    Xilinx Inc faster r-cnn (vgg16)
    Statistics of operation, parameters and mean average precision(mAP) accuracy of different object-detection models.
    Faster R Cnn (Vgg16), supplied by Xilinx Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/faster+r-cnn+(vgg16)/yolo+v5+model/pmc09600897-7-0-10
    Average 90 stars, based on 1 article reviews
    faster r-cnn (vgg16) - by Bioz Stars, 2026-10
    90/100 stars

    Images

    1) Product Images from "An OpenCL-Based FPGA Accelerator for Faster R-CNN"

    Article Title: An OpenCL-Based FPGA Accelerator for Faster R-CNN

    Journal: Entropy

    doi: 10.3390/e24101346

    Statistics of operation, parameters and mean average precision(mAP) accuracy of different object-detection models.
    Figure Legend Snippet: Statistics of operation, parameters and mean average precision(mAP) accuracy of different object-detection models.

    Techniques Used:

    Faster R-CNN 8-bit quantization result.
    Figure Legend Snippet: Faster R-CNN 8-bit quantization result.

    Techniques Used:

    Comparison of the efficiency of the convolution computation for each layer of the Faster R-CNN and the backbone network is vgg16 . The estimated time is calculated using the theoretical performance model, and the actual time is on the Faster R-CNN-vgg16 design.The hardware configure parameters is P Y n c = 14 , P M c u = 16 , P Z v e c = 8 .
    Figure Legend Snippet: Comparison of the efficiency of the convolution computation for each layer of the Faster R-CNN and the backbone network is vgg16 . The estimated time is calculated using the theoretical performance model, and the actual time is on the Faster R-CNN-vgg16 design.The hardware configure parameters is P Y n c = 14 , P M c u = 16 , P Z v e c = 8 .

    Techniques Used: Comparison

    Comparison with the state-of-the-art object detection FPGA accelerators.
    Figure Legend Snippet: Comparison with the state-of-the-art object detection FPGA accelerators.

    Techniques Used: Comparison

    Comparison with the baseline GPU imlpementation.
    Figure Legend Snippet: Comparison with the baseline GPU imlpementation.

    Techniques Used: Comparison

    Related Articles

    Comparison:

    Article Title: An OpenCL-Based FPGA Accelerator for Faster R-CNN
    Article Snippet: Faster R-CNN (vgg16) [ ] , - , 32(float) , Xilinx ZC706 , - , - , - , 200 MHz , 875 , - , - , 1.167.



    Similar Products

    faster r-cnn (vgg16)  (Xilinx Inc)
    90
    Xilinx Inc faster r-cnn (vgg16)
    Statistics of operation, parameters and mean average precision(mAP) accuracy of different object-detection models.
    Faster R Cnn (Vgg16), supplied by Xilinx Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/faster+r-cnn+(vgg16)/yolo+v5+model/pmc09600897-7-0-10
    Average 90 stars, based on 1 article reviews
    faster r-cnn (vgg16) - by Bioz Stars, 2026-10
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    Statistics of operation, parameters and mean average precision(mAP) accuracy of different object-detection models.

    Journal: Entropy

    Article Title: An OpenCL-Based FPGA Accelerator for Faster R-CNN

    doi: 10.3390/e24101346

    Figure Lengend Snippet: Statistics of operation, parameters and mean average precision(mAP) accuracy of different object-detection models.

    Article Snippet: Faster R-CNN (vgg16) [ ] , - , 32(float) , Xilinx ZC706 , - , - , - , 200 MHz , 875 , - , - , 1.167.

    Techniques:

    Faster R-CNN 8-bit quantization result.

    Journal: Entropy

    Article Title: An OpenCL-Based FPGA Accelerator for Faster R-CNN

    doi: 10.3390/e24101346

    Figure Lengend Snippet: Faster R-CNN 8-bit quantization result.

    Article Snippet: Faster R-CNN (vgg16) [ ] , - , 32(float) , Xilinx ZC706 , - , - , - , 200 MHz , 875 , - , - , 1.167.

    Techniques:

    Comparison of the efficiency of the convolution computation for each layer of the Faster R-CNN and the backbone network is vgg16 . The estimated time is calculated using the theoretical performance model, and the actual time is on the Faster R-CNN-vgg16 design.The hardware configure parameters is P Y n c = 14 , P M c u = 16 , P Z v e c = 8 .

    Journal: Entropy

    Article Title: An OpenCL-Based FPGA Accelerator for Faster R-CNN

    doi: 10.3390/e24101346

    Figure Lengend Snippet: Comparison of the efficiency of the convolution computation for each layer of the Faster R-CNN and the backbone network is vgg16 . The estimated time is calculated using the theoretical performance model, and the actual time is on the Faster R-CNN-vgg16 design.The hardware configure parameters is P Y n c = 14 , P M c u = 16 , P Z v e c = 8 .

    Article Snippet: Faster R-CNN (vgg16) [ ] , - , 32(float) , Xilinx ZC706 , - , - , - , 200 MHz , 875 , - , - , 1.167.

    Techniques: Comparison

    Comparison with the state-of-the-art object detection FPGA accelerators.

    Journal: Entropy

    Article Title: An OpenCL-Based FPGA Accelerator for Faster R-CNN

    doi: 10.3390/e24101346

    Figure Lengend Snippet: Comparison with the state-of-the-art object detection FPGA accelerators.

    Article Snippet: Faster R-CNN (vgg16) [ ] , - , 32(float) , Xilinx ZC706 , - , - , - , 200 MHz , 875 , - , - , 1.167.

    Techniques: Comparison

    Comparison with the baseline GPU imlpementation.

    Journal: Entropy

    Article Title: An OpenCL-Based FPGA Accelerator for Faster R-CNN

    doi: 10.3390/e24101346

    Figure Lengend Snippet: Comparison with the baseline GPU imlpementation.

    Article Snippet: Faster R-CNN (vgg16) [ ] , - , 32(float) , Xilinx ZC706 , - , - , - , 200 MHz , 875 , - , - , 1.167.

    Techniques: Comparison