From f47e78d53e2da67bfdc30e495e9f2acbb75c070a Mon Sep 17 00:00:00 2001 From: Emiliana <38952746+emilianavt@users.noreply.github.com> Date: Fri, 1 May 2020 11:56:31 +0200 Subject: [PATCH] Some outdated information was left in the README. --- README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 3fe1ced..3671be3 100644 --- a/README.md +++ b/README.md @@ -10,7 +10,7 @@ Thanks to [@Virtual_Deat](https://twitter.com/Virtual_Deat) and [@ENiwatori](htt [Unity sample video](https://twitter.com/emiliana_vt/status/1210622149314203648) | [Sample video](https://www.youtube.com/watch?v=AOPHiAp9DBE) | [Sample video](https://www.youtube.com/watch?v=-cBSuHGdBWQ) | [Model comparison](https://www.youtube.com/watch?v=yMcJszUy7FA) -These sample videos use older versions of the tracking models. +These sample videos use older versions of the tracking models. Tracking accuracy of the current models is better. # Usage @@ -82,9 +82,9 @@ To save both the trained model and the captured training data, type in a filenam Four pretrained models are included. Using the `--model` switch, it is possible to select them for tracking. The given fps values are for running the model on a single face video on a single CPU core. Lowering the frame rate would reduce CPU usage by a corresponding degree. * Model **0**: This is a very fast, low accuracy model. (68fps) -* Model **1**: This is a slightly slower model with better accuracy based on MobileNetV3. (59fps) -* Model **2**: This is a slower model with good accuracy based on ShuffleNetV2. (50fps) -* Model **3** (default): This is the slowest and highest accuracy model. It is based on MobileNetV3. (44fps) +* Model **1**: This is a slightly slower model with better accuracy. (59fps) +* Model **2**: This is a slower model with good accuracy. (50fps) +* Model **3** (default): This is the slowest and highest accuracy model. (44fps) FPS measurements are from running on one core of my CPU. @@ -156,7 +156,7 @@ The algorithm is inspired by: The MobileNetV3 code was taken from [here](https://github.com/rwightman/gen-efficientnet-pytorch). -For all training after the first model, a modified version of [Adaptive Wing Loss](https://github.com/tankrant/Adaptive-Wing-Loss) was used. +For all training a modified version of [Adaptive Wing Loss](https://github.com/tankrant/Adaptive-Wing-Loss) was used. * [Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression](https://arxiv.org/abs/1904.07399) by Xinyao Wang, Liefeng Bo, Li Fuxin