with skip connections and upsampling layers. No form of pooling is used, and a convolutional layer with stride 2 is used to downsample the feature maps. This helps in preventing loss of low-level features often attributed to pooling.
I have read EfficientDet paper and found that it is possible to scale my input image size during training. But the paper does not explain how resolution scaling works during inference. My question is: how is it possible that I can train a model with, say 480x480 input images and perform...
The architecture used for the image encoder is a pre-trained Vision Transformer (ViT)[8]. This is common for image processing tasks. The ViT applies a series of convolutional layers to an image to generate a set of “patches”, as shown in Figure 2. These image patches are flattened and...
Remember that before all the process starts, we break our queries, keys and values h times. This process, known as self-attention, happens separately in each of these smaller stages or 'heads'. Each head works its magic independently, conjuring up an output vector. ...
2. Convolutional Layer This is a Keras Python example of convolutional layer as the input layer with the input shape of 320x320x3, with 48 filters of size 3×3 and use ReLU as an activation function. input_shape=(320,320,3) #this is the input shape of an image 320x320x3 ...
In YOLO, the prediction is done by using a convolutional layer which uses 1 x 1 convolutions. Now, the first thing to notice is our output is a feature map. Since we have used 1 x 1 convolutions, the size of the prediction map is exactly the size of the feature map before it. In...
In order to handle the scale, SSD predicts bounding boxes after multiple convolutional layers. Since each convolutional layer operates at a different scale, it is able to detect objects of various scales. ...At large sizes, SSD seemsto perform similarly to Faster-RCNN. ...
Speaking on the new research on his personal LinkedIn profile, LeCun said that what works for ConvNeXts is “larger kernels, layer norm, fat layer inside residual blocks, one stage of non-linearity per residual block, separate downsampling layers….”. Interestingly, despite being h...
Below is how one convolutional kernel in a convolutional layer works. The kernel is applied to the image and a convolved feature is obtained. ReLU layers are used to introduce non-linearities in the neural network. Non-linearities are important because with them we can model all kind of functi...
these hand-engineered features were replaced by CNNs. Later, the pyramid itself was derived from the inherent pyramidal hierarchical structure of the CNNs. In a CNN architecture, the output size of feature maps decreases after each successive block of convolutional operations, and forms a pyramidal...