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This post contains a ranking of Steam games, based on a score intended to favor "hidden gems". A "hidden gem" is defined as a high-quality game (hence the "gem") which only got little attention (hence "hidden"). Therefore, the score of a game is defined as the product of a quality measure (its Wilson score) and a decreasing function of a popularity measure (its players total forever). The quality measure comes from SteamDB and the popularity measure comes from SteamSpy API. Finally, here is a reference to the NeoGAF post explaining the method, and the NeoGAF post explaining the idea behind the optimization of the only free parameter. The Python source code can be found on Github.

Reproducibility

To reproduce the results, use data downloaded betwe

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PC Gamer

If you arrived here from a PC Gamer article, I suggest you check the Python source code and one of these rankings:

• original ranking, featured on PC Gamer, based on data downloaded prior to the Steam summer sales.
• updated ranking, using data from June 30, a week after the Steam summer sales have started.

Bug fix regarding ranking based on playtime

Initially, I presented on this Gist page a ranking using playtime as popularity measure. However, as I have kept working on the code, I have found out this ranking likely suffered from a bug. The bug, which is now fixed, resulted in a ranking very similar to the ranking using players total as popularity measure. Thankfully, the ranking featured in the PC Gamer article is bug-free sinc

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This post contains a ranking of Steam games, based on a score intended to favor "hidden gems". It is a follow-up of a previous ranking, one week after the Steam summer sales have started. The Python source code can be found on Github.

Reproducibility

To reproduce the results, use data downloaded on June 30 with alpha ~ 10^(6.24), which arises from an optimization with 1 example of "hidden gem":

• Contradiction - the all-video murder mystery adventure (appID=373390)

Ranking of Steam games favoring hidden gems

1. Wuppo
2. fault - milestone two side:above

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pip install bumpversion setuptools wheel twine

bumpversion --current-version 0.4.0 minor setup.py
git add setup.cfg setup.py
git commit -m "Bump version to v0.5.0"

git tag "0.5.0"
git push --tags
git push

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Get pretrained weights:

wget https://dl.fbaipublicfiles.com/detr/detr-r50-e632da11.pth

Remove class weights

checkpoint = torch.load("detr-r50-e632da11.pth", map_location='cpu')
del checkpoint["model"]["class_embed.weight"]
del checkpoint["model"]["class_embed.bias"]
torch.save(checkpoint,"detr-r50_no-class-head.pth")

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import json
import time
from pathlib import Path

import steamspypi


def get_cooldown():
    cooldown = 70  # 1 minute plus a cushion

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import requests
from bs4 import BeautifulSoup


def get_my_secret_cookie():
    my_secret_cookie = {
        "SC_AUTH_UID": "30993",
        "SC_DEVICE_CATEGORY": "desktop",
        
        "SC_AUTH": "MY_SECRET_COOKIE", # TODO

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# 1. Set up StyleGAN

import dnnlib
import dnnlib.tflib as tflib
import pretrained_networks

network_pkl = 'gdrive:networks/stylegan2-ffhq-config-f.pkl'
_G, _D, Gs = pretrained_networks.load_networks(network_pkl)

Gs_kwargs = dnnlib.EasyDict()

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import pretrained_networks
import numpy as np
import dnnlib
import dnnlib.tflib as tflib
import PIL.Image

network_pkl = 'gdrive:networks/stylegan2-ffhq-config-f.pkl'
_, _, Gs = pretrained_networks.load_networks(network_pkl)

rng_seed = 5616

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Reference: https://github.com/chaofengc/PSFR-GAN

Prepare code:

%cd /content
!git clone https://github.com/chaofengc/PSFR-GAN.git

%mkdir -p /content/PSFR-GAN/pretrain_models
%cd /content/PSFR-GAN/pretrain_models
!gdown --id 1UhzMSORSul88iVfSYQYuEal83lEoNN7l