Unsplash 100 multi-labels classifier

This is a classifier trained over 100 labels of unsplash photos I trained it with the fast.ai library fast.ai The labels are:

["airplane", "ambulance", "animal", "artist", "aurora", "baby", "beach", "bear", "bedroom", "bicycle", "bird", "boats", "book", "bridge", "building", "bus", "cars", "castle", "cat", "city", "clouds", "college", "column", "concert", "couple", "crops", "dance", "dawn", "deer", "desert", "dessert", "doctor", "dog", "dolphins", "field", "fire", "floor", "food", "golf", "graffiti", "grandfather", "grandmother", "grass", "hair", "hand", "horse", "hospital", "house", "human", "insect", "kid", "library", "lights", "man", "moon", "mountain", "music", "nature", "neon", "nurse", "ocean", "painting", "palm", "party", "person", "phone", "plant", "rain", "rainforest", "restaurant", "river", "robot", "rocks", "roses", "shirt", "shop", "sign", "sky", "skyscraper", "snow", "soccer", "sports", "stadium", "staircase", "stars", "storm", "street", "sun", "sunrise", "temple", "tree", "truck", "vegetable", "water", "waves", "weed", "windows", "woman", "wood"]

Heroku Guide

Rama Blog Youtube video

Partialy based on this guide

To port the fastai-Guide: Follow this guide fastai-Guide add a Procfile and put web: python app/server.py serve in it Then in the server.py file

• import os library import os
• Set the variable: Port = int(os.environ.get('PORT', 50000))
• In uvicorn.run set port to Port uvicorn.run(app=app, host='0.0.0.0', port=Port, log_level="info") If heroku is not using the correct version of python, add a file runtime.txt with python-3.7.3

Data extraction and Model training

You can see the process of training and obtaining the data in this notebook, I use fast.ai library to train it and Unsplash api to get the data.