[this takes a while to load bear with] Geoguessr training center using computer vision to retrieve locations that have a user's weakness and providing a downloadable map that the user can practice with
No followers yet
Once you ship this you can't edit the description of the project, but you'll be able to add more devlogs and re-ship it as you add new features!
ive literally been trying for HOURS to deploy this thing but it takes up too much memory for any free services like im actually crashing out
okay i create a function that creates a json file that geoguessr can read, and I tested it and looks like it works! (somewhat). The location spread is really weird and not all locations are within the selected country. also to go through 500 locations takes 10 min which is a bit too long, so I need to figure out how to make that shorter. but all things considered, pretty good progress so far
currently working on applying my ml model to filter out locations to include in maps. also trying to figure out how to do this the most effectively without downloading images, taking up too much space, etc. sort of just doing trial and error and debugging along the way
i set up the backend with a flask app and now the frontend posts the user's data to the backend. next step is figuring out how to connect this data and the ml model
frontend!! I worked on this for about an hour and a half, so i lowkey forget what the other hour was for ngl. but for the frontend i did the design and interactive adding/deleting functions of user weaknesses. next step is to send that data to a backend prob flask app
trained and tested the model locally using yolov5. now need to collect more images with only bollards using the model to add to my dataset to make it more reliable
i trained a model on roboflow, but now trying to retrain a model locally as I realized roboflow would cost a lot. im going to use yolov5, and I havent done any machine learning stuff like this before so lets see how long it takes lol
I picked Mapillary and its API to retrieve images to help train my model. Right now, I am just focusing on bollard object recognition as a test round and labelling some data.