Row 75143

Row ID: 75143 | Dataset Entry | Axioma AXP Content Repository

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I'm struggling with detecting objects that have the same shape but different colors, with no other distinguishing features. When there are distinguishable patterns, CNN-based architectures like YOLO work wonders and achieve high accuracy. However, I need a method that can accurately classify objects based purely on color.

My current challenge is that these objects are not separable when I try to segment them by color in RGB space. Does anyone have suggestions or methods that achieve good accuracy in determining object classes by color?

I've included an image below for reference. Any help would be greatly appreciated!

https://preview.redd.it/83c6e7dbbb2d1.png?width=793&format=png&auto=webp&s=532c7cffcbaea96eb48d374e073bd49d5f029212

# Edit

Transforming the colorspace to HSV solved the problem. Below is the HSV colorspace representation

https://preview.redd.it/hq5yrf5wcf2d1.png?width=793&format=png&auto=webp&s=c40002fd360c1891baaed915f536aa7dae4061f5

In the first stage, I used YOLO model to detect the objects

In the second stage, I cropped the detected objects, converted the cropped images to HSV, calculated the average component value per object and then trained XGBoost model to predict color label based on 3D vector, representing the average values of H, S, and V channel.

FieldValue
text I'm struggling with detecting objects that have the same shape but different colors, with no other distinguishing features. When there are distinguishable patterns, CNN-based architectures like YOLO work wonders and achieve high accuracy. However, I need a method that can accurately classify objects based purely on color. My current challenge is that these objects are not separable when I try to segment them by color in RGB space. Does anyone have suggestions or methods that achieve good accura…
label r/machinelearning
dataType post
communityName r/MachineLearning
datetime 2024-05-24
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url_encoded Z0FBQUFBQm5Lak95SzNNQk5ORF9UYkUxdTJQVUVBUlh1VVEzZ3Qwb3pPV3F5TWhDTEx2ZGJXaDJ1aWRnZHBEellQSzRnZDBvZGRrcXZWQlhnNWFvUE1wY3JLbVlCdlE0Y2ZNQ2xCbDF5TVRVY2J5WUV6SkMySERLRVh3and2WGd2WEk1T00xd0RNZmVGN2F1YXVUSm81bUQzZzFJYVRmUDl5LURWbjl4aUludDFtYWJFY3ctNmlZVWtaRjRWX3JMcWRQMmtLR2pJYmU4c1kzTU8tcHo1b3hrUk5ZME9RdWc3dz09

Raw Record

{
  "text": "I'm struggling with detecting objects that have the same shape but different colors, with no other distinguishing features. When there are distinguishable patterns, CNN-based architectures like YOLO work wonders and achieve high accuracy. However, I need a method that can accurately classify objects based purely on color.\n\nMy current challenge is that these objects are not separable when I try to segment them by color in RGB space. Does anyone have suggestions or methods that achieve good accuracy in determining object classes by color?\n\nI've included an image below for reference. Any help would be greatly appreciated!\n\nhttps://preview.redd.it/83c6e7dbbb2d1.png?width=793&format=png&auto=webp&s=532c7cffcbaea96eb48d374e073bd49d5f029212\n\n# Edit\n\nTransforming the colorspace to HSV solved the problem. Below is the HSV colorspace representation\n\nhttps://preview.redd.it/hq5yrf5wcf2d1.png?width=793&format=png&auto=webp&s=c40002fd360c1891baaed915f536aa7dae4061f5\n\nIn the first stage, I used YOLO model to detect the objects\n\nIn the second stage, I cropped the detected objects, converted the cropped images to HSV, calculated the average component value per object and then trained XGBoost model to predict color label based on 3D vector, representing the average values of H, S, and V channel.",
  "label": "r/machinelearning",
  "dataType": "post",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-24",
  "username_encoded": "Z0FBQUFBQm5Lak1pUE5oZ3hXc3dNU01qQWROdU1ULWRsX09NMEtOUllOUEx2M2t4WG5OaDdTVXo3WGNEdnFmQXpkelZ4MWJDYmpqVVFTd3k4SUpaM1ZwZTUwUFI1dnEtQWc9PQ==",
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}
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