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This is easy:
unk__2241 is batch size, so its unknown for now.
After CSPDarknet53, your output shape is (unk_2241,13,13,512) as reduce factor is 32. Then after SPP you have (unk_2241, 13, 13, 2048) with kernel size = [1,3,5,13].
You have 3 heads for detecting large, medium and small objects on image and 3 anchors per each of size of object. In this 3 heads yolo uses 3 feature maps which comes from modified PANet and this feature maps are (unk_2241,52,52,256), (unk_2241,26,26,512), (unk_2241,13,13,1024) as reduce factor is 8, 16, 32.
Content Under CC-BY-SA license WEBJul 7, 2021 · YOLO head output shapes: (unk_2242, 52, 52, 3, 7), (unk_2245, 26, 26, 3, 7), (unk_2248, 13, 13, 3, 7). Batch size could be …
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- The model is run with the resized image as input with a shape=(1,608,608,3).
The model provides 3 output layers 139, 150 and 161 with the shapes respectively (1, 76, 76, 255), (1, 38, 38, 255), (1, 19, 19, 255). The number of channels is 255 = ( bx,by,bh,bw,pc + 80 classes ) * 3 anchor boxes, where (bx,by,bh,bw) define the position and size of the box, and pc … - 3 anchor boxes per Yolo output layers are defined:
•output layer 139 (76,76,255): (12, 16), (19, 36), (40, 28)
- The model is run with the resized image as input with a shape=(1,608,608,3).
- Question & Answer
WEBJan 4, 2024 · Relative to inference speed, YOLOv4 outperforms other object detection models by a significant margin. We have recently been amazed at the performance of …
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WEBJun 2, 2020 · The model provides 3 output layers 139, 150 and 161 with the shapes respectively (1, 76, 76, 255), (1, 38, 38, 255), (1, 19, 19, 255).
WEBDec 23, 2021 · Selection criteria are based on the optimal balance between input network resolution (input image size), number of convolution layers, number of parameters, and number of output …
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