Answers.Body: <p>Duplicate boxes with unique labels make the problem too complex for the model. What I suggest is you use the horse face detection model to get a bounding box of the horse's face, crop the face image and use that image as a training sample for a separate classification model.</p>
  <p>I have seen  this method used often in human identification, and dividing the tasks/models seems much more reasonable than trying to solve it in one model.</p>
  <p>P.S. Just out of curiosity, you said that</p>
  <blockquote>
  <p>the best way to distinguish one horse from another is by its face</p>
  </blockquote>
  <p>Is this really true? Aren't there better features to use from the body?</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-11-17T04:22:23.377Z
Answers.Text: Do you have any recommendations for generating the classification model?
  
  As far as whether or not the face is the best way to identify them, I think it's practically the easiest. The legs sometimes are unique, but those aren't always well captured in a photo (especially a portrait).
Answers.UserDisplayName: GoldenNewby
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-11-17T04:27:20.920Z
Answers.Text: @GoldenNewby A simple cnn classifier would suffice for a baseline.
Answers.UserDisplayName: DKDK
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-11-17T02:21:42.187Z
Answers.LastActivityDate: 2021-11-17T02:21:42.187Z
Answers.UserDisplayName: DKDK
Body: <p>My goal is to identify the horse in a photo. I'm dealing with about 500 unique horses.</p>
  <p>My feeling is that the best way to distinguish one horse from another is by its face. So I trained Yolov5 successfully to find faces at reasonable angles.</p>
  <p>I'd like to take this a step further, and teach it to identify <em>which</em> horse's face it sees.</p>
  <p>I'm new to this sort of thing (though not programming in general), so the way I assume I should approach this is to add an additional label like face_horsename, with the unique name for the horse (or really, a unique reference to a database of horses).</p>
  <p>Is that the right approach? It seems like the Yolo file format doesn't allow for multiple labels for the same box, so my guess is I should just make 2 rectboxes that are identical, but both point to different labels.</p>
  <p>Frankly, I'd like to take it even further and label the same thing with the type of &quot;blaze&quot; of the horse's face, and its proper name for the horse's color. So now I'm talking about 4 labels.</p>
  <p>Is that the right approach (duplicate boxes with unique labels)?</p>
  
Tags: <machine-learning><image-recognition><object-detection><object-recognition><yolo>
Title: Multiple labels for the same rectbox?
UserDisplayName: GoldenNewby
ThreadText: # Multiple labels for the same rectbox?
  
  (Asked by: username_0 on 2021-11-16)
  
  My goal is to identify the horse in a photo. I'm dealing with about 500 unique horses.
  
  My feeling is that the best way to distinguish one horse from another is by its face. So I trained Yolov5 successfully to find faces at reasonable angles.
  
  I'd like to take this a step further, and teach it to identify *which* horse's face it sees.
  
  I'm new to this sort of thing (though not programming in general), so the way I assume I should approach this is to add an additional label like face\_horsename, with the unique name for the horse (or really, a unique reference to a database of horses).
  
  Is that the right approach? It seems like the Yolo file format doesn't allow for multiple labels for the same box, so my guess is I should just make 2 rectboxes that are identical, but both point to different labels.
  
  Frankly, I'd like to take it even further and label the same thing with the type of "blaze" of the horse's face, and its proper name for the horse's color. So now I'm talking about 4 labels.
  
  Is that the right approach (duplicate boxes with unique labels)?
  
  **Tags:** machine-learning, image-recognition, object-detection, object-recognition, yolo
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2021-11-17. Score: 1 | Accepted answer
  
  Duplicate boxes with unique labels make the problem too complex for the model. What I suggest is you use the horse face detection model to get a bounding box of the horse's face, crop the face image and use that image as a training sample for a separate classification model.
  
  I have seen this method used often in human identification, and dividing the tasks/models seems much more reasonable than trying to solve it in one model.
  
  P.S. Just out of curiosity, you said that
  
  > the best way to distinguish one horse from another is by its face
  
  Is this really true? Aren't there better features to use from the body?
  
  > **Comments:**
  > **username_0**: Do you have any recommendations for generating the classification model?  As far as whether or not the face is the best way to identify them, I think it's practically the easiest. The legs sometimes are unique, but those aren't always well captured in a photo (especially a portrait).  
  > **username_1**: @GoldenNewby A simple cnn classifier would suffice for a baseline.  
  
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