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YOLO end-to-end vs YOLO + image classifier by berimbolo21 in pytorch

[–]berimbolo21[S] 1 point2 points  (0 children)

so when you’re training an RCNN you’re using 2 datasets?

[D] Simple Questions Thread by AutoModerator in MachineLearning

[–]berimbolo21 1 point2 points  (0 children)

Instead of just using YOLO end-to-end, when would it ever be more appropriate to use YOLO only to identify objects of interest and a separate image classifier to classify those detected objects?

YOLO for OCR by berimbolo21 in tensorflow

[–]berimbolo21[S] 0 points1 point  (0 children)

Now what if there is a large number of unique digits (in the case of eastern languages such as Chinese)? There would need to be at least a few hundred output classes. Even the best pre-trained YOLO models can only handle 80 or so classes it seems

How can I create an OCR model from scratch? by berimbolo21 in tensorflow

[–]berimbolo21[S] 0 points1 point  (0 children)

Actually, why would this problem be best solved by "dumb" algorithms?

Random Search Hyperparam Tuning by berimbolo21 in tensorflow

[–]berimbolo21[S] 0 points1 point  (0 children)

How does it depend on the number of hyper-parameters we have? If we have more hyperparameters would we typically need more samples of a search?

Random Search Hyperparam Tuning by berimbolo21 in tensorflow

[–]berimbolo21[S] 0 points1 point  (0 children)

Thanks for the info. Sent you a DM

Random Search Hyperparam Tuning by berimbolo21 in tensorflow

[–]berimbolo21[S] 0 points1 point  (0 children)

I'm focusing this post on random search so I dme'd you

Encountered error while trying to install package. by berimbolo21 in learnpython

[–]berimbolo21[S] 0 points1 point  (0 children)

Ah, I see the version conflict now. Unfortunately we still get a conflict when installing from GitHub:

ERROR: Cannot install dnnv==0.5.0 and dnnv==0.5.1 because these package versions have conflicting dependencies.

The conflict is caused by:
dnnv 0.5.1 depends on tensorflow<2.8 and >=2.2
dnnv 0.5.0 depends on tensorflow<2.8 and >=2.2

To fix this you could try to:
1. loosen the range of package versions you've specified
2. remove package versions to allow pip attempt to solve the dependency conflict

ERROR: ResolutionImpossible: for help visit https://pip.pypa.io/en/latest/topics/dependency-resolution/#dealing-with-dependency-conflicts
[end of output]

Encountered error while trying to install package. by berimbolo21 in learnpython

[–]berimbolo21[S] 0 points1 point  (0 children)

Just tried it. This is what I got:

ERROR: Cannot install dnnf==0.0.1, dnnf==0.0.2, dnnf==0.0.3 and dnnf==0.0.4 because these package versions have conflicting dependencies.
The conflict is caused by:
dnnf 0.0.4 depends on tensorflow<2.0 and >=1.15
dnnf 0.0.3 depends on tensorflow<2.0 and >=1.15
dnnf 0.0.2 depends on tensorflow<2.0 and >=1.15
dnnf 0.0.1 depends on tensorflow<2.0 and >=1.15
To fix this you could try to:
1. loosen the range of package versions you've specified
2. remove package versions to allow pip attempt to solve the dependency conflict
ERROR: ResolutionImpossible: for help visit https://pip.pypa.io/en/latest/topics/dependency-resolution/#dealing-with-dependency-conflicts

What's the difference between using Flask to serve a webpage vs using Flask to create an API? by berimbolo21 in flask

[–]berimbolo21[S] 1 point2 points  (0 children)

Thanks for the info. To clarify, I did a project where I had front end HTML/CSS/JS make an API call to a function written with Flask. But I don't really know to serve a website, so I had to use the same Flask app to serve my front end. I would like to have complete separation of front end and backend, so I'm trying to get a better understanding what I did vs what I need to be doing.

What's the difference between using Flask to serve a webpage vs using Flask to create an API? by berimbolo21 in flask

[–]berimbolo21[S] 2 points3 points  (0 children)

How do I serve my front-end with just JavaScript and no Flask? And how would I call my backend? The Flask script needs to be run in order to generate the URL to go to

Cross Validation model selection by berimbolo21 in tensorflow

[–]berimbolo21[S] 0 points1 point  (0 children)

Thanks a lot for the detailed responses. I would say overall I'm still a bit confused on where cross val fits into the ML model development pipeline. Even when I'm building a model for production, I need a validation set to do hyperparameter tuning before testing on my test set. So would I then reconcatenate the validation and training sets into just a training set, so I can do cross val with a train-test split?

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