I need a python library for motorcycle detection using tensorflow

I need a python library for motorcycle (with 2 people ) detection using tensorflow Sample: [login to view URL] ## Description: I need a python library able to: - Receive a [login to view URL] file with videos (dataset) inside divided into test and train - A method to start the training process - A method to query the training status - A method to export the training result - A method to receive the frames from RTSP (The Real-Time Streaming Protocol) and return if the pattern is present on the - video. Example [login to view URL] - A video explaining all your workspace and how to change the model used and how to add more parameters to improve the accuracy ## Mandatory: - GITHUB will be used. Is not accepted other platform for source code management - Deliverables on each milestone. - Use docker and the latest versions of python and tensorflow. - I DON’T HAVE VIDEOS AS DATASET. TAKE IN ACCOUNT FOR YOUR ESTIMATION - Python 3.8 and a compatible Tensorflow version should be used - Developer should have to emulate an ip camera for tests - Help could be required at the moment of library usage ## Architecture - Python code should be created oriented to a library. Read this [login to view URL] - The library should be installed in another python project - It should work on Linux ## Methods - uploadDataset(“/tmp/[login to view URL]”) Receives a [login to view URL] file with this structure [login to view URL] Returns an uuid pointing to the location of received dataset - startTraining({ "epochs": 25, "dataset_uuid": "fffb42f1-4b83-db259ca3850c”}) Receives some settings like epoch and the dataset uuid. Due to the training may take a while, this should be an async task Returns an uuid pointing to the started job (training) - getTrainingStatus("901b9311-9876-4539-83ac-80c8b919282f”) Receives the training uuid Returns a dictionary with these values { "status":"in-progress", "progress": "0% - 100%"} - exportKnowledge(“/tmp/[login to view URL]”) Receives a location in which the knowledge files should be compressed (success training result) - detectClassFromFrames("class-01-motorcycle-2-people", frame) Receives a frame obtained from frame = [login to view URL]() Check [login to view URL] Returns a dictionary with these values {“accuracy”: 85 , “detection” : {“payload”: [100,451, 200, 451]} } Coordinates should be the square in video in which the motorcycle appear - detectClassFromFrames("vehicle-license-plate", frame) Receives a frame obtained from frame = [login to view URL]() Check [login to view URL] Returns a dictionary with these values {“accuracy”: 85 , “detection” : {“payload”: “37-N1 4635”} } Sample of plate: [login to view URL] ## Deliverables On each milestone - github push to main branch - video as demo of how it works - video explaining the created source code ## Workflow - Milestones of the backlog, should be estimated(time) by the developer - Developer starts some milestone, develop and notify the task completion with the explained deliverables - Developer should wait until the Project Manager (me) validate the feature - If there are no errors: - Project Manager will generate a git tag - Developer should start the next milestone - If there are errors: - Developer should fix them before starting the next task ## Acceptance tests - Application should be tested on real ip cameras ## Milestones [login to view URL] ## Note Please, read carefully and ask me anything you need to avoid misunderstandings. Check the milestone sheet, copy it and share with me your estimation (time and budget for each milestone). Let’s start ASAP, this is the first sprint