1adrianb / 1adrianb/face-alignment-training

Error in building dataset, confusion on training data directory structure?

Abierto
#17 3 comentarios 2 reacciones 0 asignados Ver en GitHub
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Lua
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152
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Descripción

Hi, thanks for posting the code for the face alignment training, really appreciate the awesome work!

I'm trying to simply recreate the training results for my own understanding, although I'm having trouble understanding the initial instructions

I've downloaded the `LS3D-W` dataset from https://adrianbulat.com/face-alignment
and after untarring I see a directory with
```
images/
|---- 300VW-3D
|---- 300VW-Testset-3D
|---- AFLW2000-3D-Reannotated
|---- Menpo-3D
|---- plot.lua
|---- Readme.txt
```
After downloading the `landmarks.zip` from https://www.adrianbulat.com/downloads/FaceAlignment/landmarks.zip

Here's where I begin to get confused... When I unzip the folder I get
```
landmarks/
|---- AFW
|---- HELEN
|---- IBUG
|---- LFPW
```

Firstly, when I run `th main.lua`, what exactly is the `path_to_300W_LP_dataset`? From the names I'm unsure which is the correct directory.
Furthermore, the directory searching code within `dataloader.lua` at `DataLoader.getDataFaces(opt, split)` fails to correctly build the dataset?

Perhaps I've downloaded the wrong directory, or have misunderstanding on which dataset to be downloading?

Thanks in advance for the clarification,
Joseph

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Línea de trabajo

The issue is about dataset directory structure confusion for training. Look at dataloader.lua's DataLoader.getDataFaces function to understand expected paths. Compare with the downloaded LS3D-W and landmarks directories. Determine the correct path_to_300W_LP_dataset by matching dataset names in code to folder names. Run th main.lua with different paths to see which one works.

Escrito por el modelo de indexación a partir del texto del issue.

Evaluación

Stack tecnológico
lua
Área
data, machine-learning
Tipo de issue
Error
Dificultad
3/5
Tiempo estimado
1-2 días
Estado de actividad
Estancado
Claridad
Bastante claro
Aptitud para principiantes
45/100

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