classify | like Module & Class Inheritance Library

I have a dataset of around 2800 records. I have a column called as 'Trigger-catogory' which is a Multi-class classification field with one of the attribute being 'CLI-Related' The dataset has a 'Headline' column and going through the headline i want to further classify it . I have written a code as below

I was reading a decent paper S-DCNet and I fell upon a section (page3,table1,classifier) where a convolution layer has been used on the feature map in order to produce a binary classification output as part of an internal process. Since I am a noob and when someone talks to me about classification I automatically make a synapse relating to FCs combined with softmax, I started wondering ... Is this a possible thing to do? Can indeed a convolutional layer be used to classify a binary outcome? The whole concept triggered my imagination so much that I insist on getting answers...

Honestly, how does this actually work? What is the difference between using a convolution filter instead of a fully connected layer for classification purposes?

Edit (Uncertain answer on how does it work): I asked a colleague and he told me that using a filter of the same shape as the length-width shape of the feature map at the current stage, may lead to a learnable binary output (considering that you also reduce the #channels of the feature map to a single channel). But I still don't understand the motivations behind such a technique ..

I am running a FastAPI (v0.63.0) web app that uses SpaCy (v3.0.5) for tokenizing input texts. After the web service has been running for a while, the total memory usage grows too big, and SpaCy throws MemoryErrors, results in 500 errors of the web service.

I'm curious to see if anyone has any thoughts on how to accomplish this in Python with Pandas.

I have a dataframe (df1) with the credit card transaction details that contains the Point of Sale description (df1['Description']) and amount (df1['amount']). The POS description is unique to each location so you end up with several descriptions for Amazon, Shell Oil, etc.

I have another dataframe (df_lookup) that will serve as a lookup table to classify the transactions. This dataframe will include the name (df_lookup['Name']) and the category to classify each transaction df_lookup['Category']).

This is what I would like to accomplish: Compare the df1['Description'] to df_lookup['Name']. If the df1['Description'] contains the df_lookup['Name'], the corresponding df_lookup['Category'] will be added to df1 as a new column df1['Category']. Please see the examples of each dataframe and the desired outcome below.

Example of df1:

Example of df_lookup:

Desired Output to df1:

I have a large DataFrame of distances that I want to classify.

I have a query:

I am trying to implement GoogleNet inception network to classify images for classification project that I am working on, I used the same code before but with AlexNet network and the training was fine, but once I changed the network to GoogleNet architecture the code kept throwing the following error:

I have two data frames, df1 and df2, and want to know if something like the following is (easily) possible:

For every df1$id that matches df2$id, I want to compare df1$day against df2$day and either classify them as MATCH or NO MATCH in a new column (df1$matched) depending on whether they are identical or not.

Further clarification:

If a value in df1$id matches / appears in df2$id, I then want to look at df1$day and df2$day against that particular id. Next, I want to compare the values from df1$day and df2$day to see if they are the same or different. I then want to create a new column (matched) which classifies these values based on whether or not they are the same. Thus, for every id match between df1 and df2, the algorithm should output something like this:

Sample data:

When the camera opens a blank camera appears for a few seconds and it always gives the same below output and stops.

prediction: [{"className":"nematode, nematode worm, roundworm","probability":0.050750732421875},{"className":"matchstick","probability":0.043731689453125},{"className":"lighter, light, igniter, ignitor","probability":0.021453857421875}]

Any idea how I can make the real time prediction work? without getting a false prediction as above just for one time

Below is the Camera Screen code where the prediction should happen in real time camera feed when user scans a certain surrounding