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Dice: d4, d6, d8, d10, d12, d20 Images
许可协议: CC-BY-SA 4.0

Overview

Context

Beginner set of 16,000 custom images for categorizing polyhedral dice

Content

Image Organization

~ 85% / 15% (train / valid)

  • all training images are 480x480
  • all d4, d8, d10, and d12 validation images are 480x480
  • most d6 and d20 validation images are 480x480
  • a small percentage of additional d6 and d20 validation images are larger (1024px long side) and completely unlike the training set

Methodology

All images were created, edited, and sorted by Mario Lurig.

  • Fixed camera positions (minimum 2 angles) used to capture video on a rotating platform with two white lights
    • Minimum 5 different dice used on 6 different backgrounds (white and various colors)
    • Video was then exported as images and then batch cropped to 480x480
  • Handheld camera moved over 5+ dice on various wood surfaces (minimum 2) using natural lighting
    • Video edited and exported to images then batch cropped to 480x480
    • Images that were partially out of crop were manually removed

The additional d6 and d20 validation images were from my personal image collection or taken additionally on a variety of surfaces with no care for lighting conditions to work as a more robust test.

The validation images were pulled from the full image set (480x480 images) as a 1/7th slice rather than randomly. If preferred, you could combine train/valid together and randomly assign them via your code; this data organization method was chosen to help beginners.

Finally, images taken in like groups are named in like ways. For instance, d4_angleXXX are all d4 dice taken at an angle. d10_top are all d10 dice taken from the top down. Once again done in an effort to make it easy to add/remove data and see how that changes the results.

Note: There are more d6 and d20 images than d4,d8,d10,d12 due to those two dice being my initial test set before building the rest.

Inspiration

As an avid roleplayer and the person behind HeroMuster.com, I decided to start learning ML from not only the code and execution side, but also from the data collection/organization side. This felt like a great way to do that.

Quicky results

ResNet101 0.9947 accuracy
Confusion Matrix

数据概要
数据格式
image,
数据量
16.387K
文件大小
84.45MB
发布方
Mario Lurig
| 数据量 16.387K | 大小 84.45MB
Dice: d4, d6, d8, d10, d12, d20 Images
许可协议: CC-BY-SA 4.0

Overview

Context

Beginner set of 16,000 custom images for categorizing polyhedral dice

Content

Image Organization

~ 85% / 15% (train / valid)

  • all training images are 480x480
  • all d4, d8, d10, and d12 validation images are 480x480
  • most d6 and d20 validation images are 480x480
  • a small percentage of additional d6 and d20 validation images are larger (1024px long side) and completely unlike the training set

Methodology

All images were created, edited, and sorted by Mario Lurig.

  • Fixed camera positions (minimum 2 angles) used to capture video on a rotating platform with two white lights
    • Minimum 5 different dice used on 6 different backgrounds (white and various colors)
    • Video was then exported as images and then batch cropped to 480x480
  • Handheld camera moved over 5+ dice on various wood surfaces (minimum 2) using natural lighting
    • Video edited and exported to images then batch cropped to 480x480
    • Images that were partially out of crop were manually removed

The additional d6 and d20 validation images were from my personal image collection or taken additionally on a variety of surfaces with no care for lighting conditions to work as a more robust test.

The validation images were pulled from the full image set (480x480 images) as a 1/7th slice rather than randomly. If preferred, you could combine train/valid together and randomly assign them via your code; this data organization method was chosen to help beginners.

Finally, images taken in like groups are named in like ways. For instance, d4_angleXXX are all d4 dice taken at an angle. d10_top are all d10 dice taken from the top down. Once again done in an effort to make it easy to add/remove data and see how that changes the results.

Note: There are more d6 and d20 images than d4,d8,d10,d12 due to those two dice being my initial test set before building the rest.

Inspiration

As an avid roleplayer and the person behind HeroMuster.com, I decided to start learning ML from not only the code and execution side, but also from the data collection/organization side. This felt like a great way to do that.

Quicky results

ResNet101 0.9947 accuracy
Confusion Matrix

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