I was able to run the code without "any" modifications on Tensorflow 2.4.0, just had to replace the imports:
from keras.datasets import mnist
import tensorflow.keras as keras
from tensorflow.keras.datasets import mnist
469/469 [==============================] - 4s 7ms/step - loss: 2.2914 - accuracy: 0....
The general purpose of stride (along with padding) is to determine the spatial dimensions of the output. So, with appropriate stride (and padding), you can also make the spatial dimensions of the output volume bigger than the ones of the input volume. In fact, transpose convolution, which is used e.g. in the context of convolutional auto-encoders, is based ...
Do they mean the strides that are related to the CNN, pooling, etc., or are they referring to any other stride information?
The stride referred to by the quote "only plays with the size and stride information at the tensor level" is referring to internal storage of tensors. Luckily in most normal conversations about AI logic you do not care about ...
I misread the question; It seems like OP is more interested in pose tracking. So, I'll have to point OP to papers on that, like this one. Using multiple frames becomes especially important when there are multiple people in the frame, and it's desired to track which pose belongs to which person.
For more papers on pose tracking, look here.
I don't think there is a great difference between pose estimation in pictures and in videos.
Do you know MediaPipe? https://google.github.io/mediapipe/
MediaPipe does perform an tracking from the keypoints through time. So the temporal information is used. Apart from that it is the same problem as the estimation from keypoints in static images.
As I know about the YOLO, it's algorithm splits the whole picture into many small frames and perform classification and boundaries detection at once for every frame, so that the location of the object is not a matter.
An interesting issue: the model is seemingly under-fitting to the train data (since the accuracy isn't that high), but it is also underfitting to the training data. :)
When you flatten the data and use dense layers, you lose all of the spatial information. Before you do that, you might want to subtract the left side of the output (I calculated it having a ...
No, Feature Pyramid Networks does not only refer to CNNs.
Take the recently trending "Swin Transformer" as an example.
Swin Transformer is a variant of vision transformers, and they do not employ convolutional layers. However, their design allows them to output feature maps in multiple scales (feature pyramids). The authors actually used the ...
In any case anyone is struggling with the same problem. It seems that they were simply typos in the original paper. I have downloaded the author's framework Darknet, as well as the configuration and weight files for YOLOv1.
Then, the architecture can be tested with one sample image using this command:
./darknet yolo test cfg/yolov1/yolo.cfg yolov1.weights ...