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I want to build my own Faster-RCNN model from scratch for multi-object detection from image data.
Can somebody please refer me good sources to step by step approach to implement faster-RCNN?
Which one will be good YOLO or faster-RCNN in terms of accuracy and execution time?
If you are in computer vision go through https://www.pyimagesearch.com/ guy named Adrian has great work over there
Instead of starting from scratch use pre-build model as base model afterward you can
go for implementation of your own intermediate layer
The architecture of faster RCNN
https://medium.com/#smallfishbigsea/faster-r-cnn-explained-864d4fb7e3f8
Actual implementation source -1
Actual implementation source-2
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I am new to NLP and started with Spacy. I want to train NER with custom data and I am looking for free tool that can be used for Annotation.
Please suggest if you are using any open-source and User-friendly tool
Thanks in advance
You can start labeling with:
https://labelstud.io, or
https://github.com/doccano/doccano
The Spacy team also has the Prodigy (https://prodi.gy/), which could be freely used in academia.
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Is there any open source project or free avaliable source where I can query a webpage's category type (like https://www.trustedsource.org/en/feedback/url). I have more than 200K webpage in my dataset.
To me it looks like more of a classification problem which is suitable for Machine Learning. For this purpose you can make your model in popular ML frameworks (such as Keras/TensorFlow and PyTorch) or search for available ones on internet and use your dataset to do a transfer learning.
I could find a project on GitHub (link) that can be a good starting point.
Hi today and happy weekend!
that's interesting to know if a category is used as category pages, since google shows up multiple spots of one domain when it has category pages.
Examples:
danlok(com)
best example to see: bloomberg....
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I am a newbie of calabash-android. i did learn how to write scripts for automation testing. please refer me some "books" or "articles" to learn how to write scripts in a optimized way.
I recently started a new job as an automated tester for a mobile app and found the following book a good introduction to the Cucumber framework:
https://pragprog.com/book/hwcuc/the-cucumber-book
It doesn't go into lots of detail about Calabash specifically but does have lots of information on writing tests in general.
Once you have your feature files in place you just write the underlying code (Ruby in my case) to make the app do what you want (ie. touch, swipe).
It's also good to use:
query('*') whilst in the calabash-android console. It dumps out the all the information you need to know for example what ID's and text to check for on any given screen.
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I am looking for training data for text classification into categories like sports, finance, politics, music, etc.
Please guide to references. Hello.
You can get a Reuters corpus by applying at Reuters
You can also get the Technion Text Repository TechnionRepo
If you are building a text classfication system in real time, you would be already having a corpus of documents. One of the assumption in any Classifier is, training data & test data are similar or from the same distribution.
If you are just exploring or building sample usecases in this area, then probably this link might be helpful to get some train data.
http://web.ist.utl.pt/acardoso/datasets/
http://disi.unitn.it/moschitti/corpora.htm
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I want to determine which part of audio file contain speech or music.
I hope someone has a made something like this or can tell me where to start.
Can you please suggest some method/tutorial for doing the same.
Thank you.
Check out the pyAudioAnalysis python library. Among others, it has a pre-trained speech-music classifier and two segmentation-classification methods (one based on fix-sized windows and another based on HMMs).
You can extract speech and music parts of an audio recording quite easily, e.g.:
from pyAudioAnalysis import audioSegmentation as aS
[flagsInd, classesAll, acc] = aS.mtFileClassification("data/scottish.wav", "data/svmSM", "svm", True, 'data/scottish.segments')
with a result as the one in this image
There's lots of prior art in this area, but I'd suggest browsing through some of Dan Ellis's papers. The slides for this talk has some good background. In short it's all down to picking the right feature vectors.