COVID-19 detection; X-ray images; Canny edge detector; Grad-CAM; deep learning1. Introduction Lung abnormalities, including ARDS, lung cancer, pneumothorax, and pneumonia, pose significant health challenges across all age groups. The emergence of the SARS coronaviruses, particularly the COVID-19 ...
CNN is built on layers called convolution (Conv), pooling, the rectified linear unit (ReLU) equation, fully connected (FC), batch normalization (BN), and global pooling (GP) [50]. CNN employs a mathematically unique convolution process in at least one layer. When convolution and neural ...
Augmenting Self-attention with Persistent Memory https://arxiv.org/abs/1907.01470 Proposes adding learned memory key / values prior to attention. They were able to remove feedforwards altogether and attain similar performance to the original transformers. I have found that keeping the feedforwards ...
Variable size inference is replaced with fixed size inference as preferred by edge devices. E.g. tflite models are exported with a fixed i/p size. Training and Testing Training any model using this repo will take the above changes by default. Same commands as the official one can be used...
Pneumonia is one of the diseases that causes the most fatalities worldwide, especially in children. Recently, pneumonia-caused deaths have increased dramatically due to the novel Coronavirus global pandemic. Chest X-ray (CXR) images are one of the most r
ModelDense, use_xnnpack=falseConv, use_xnnpack=falseConv, use_xnnpack=true MobileViT_S Inference (avg) 215371 us Inference (avg) 163836 us Inference (avg) 163817 us EfficientFormerL1 Inference (avg) 126829 us Inference (avg) 107053 us Inference (avg) 107132 us model_surgery.convert_extract_...
>>> import mindspore.nn as nn >>> from mindspore.common import initializer >>> from mindspore.common.initializer import XavierUniform >>> >>> weight_init_1 = XavierUniform(gain=1.1) >>> conv1 = nn.Conv2d(3, 6, weight_init=weight_init_1) >>> weight_init_2 = XavierUniform(gain=1....
MaxPooling2D-1 31x31 2x2 32 0 Conv2D-2 29x29 3x3 64 18,496 MaxPooling2D-2 14x14 2x2 64 0 Flatten 1x12544 – – 0 Dense-1 1x128 – – 1,605,760 Class-4 Dense-2 1x21 – – 2709 SoftMax 1x4 – – 0 Class-3 Dense-2 1x15 – – 2547 SoftMax 1x3 – – 0 Class-2 ...
这里用高斯模糊代替defums_noise_reduction(image):returncv2.GaussianBlur(image,(3,3),0)classImagePreprocessing(nn.Module):def__init__(self):super(ImagePreprocessing,self).__init__()defforward(self,image_path):# 读取图像image=cv2.imread(image_path)# 应用边缘检测edge_detected_image=sobel_edge_...
[x // 2 for x in k] # auto-padreturn pclass Conv(nn.Module):# Standard convolutiondef __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups"""Standard convolution conv+BN+act:params c1: 输入的channel值:params c2:...