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Model Description
Pills can often be hard to identify at a glance, and they can sometimes be mixed up with others as a result. This can be dangerous as taking the wrong pills by accident can have serious health effects. Having some kind of detection model between different pills can help identify what each pill is to help ensure additional safety
Dataset & Procedure
This uses the dataset “Pill Detection Computer Vision Model” by Mohamed Attia, with photos also curated by them from Roboflow Universe. The dataset can be found here. In the original dataset, images were auto-oriented, resized, and stretched to 416x416. The images are of pills of various brands and colors on different backgrounds and backdrops. The dataset contains 496 images total, with 351 train, 98 valid, and 47 test.
The dataset was manually combed through on its annotations to double check and validate each image to ensure that the quality of the annotations is up to par. The annotations were not modified as they were all found to be accurate after manual validation.
Epochs: 350, but exited prematurely at 123 due to results starting to plateau Batches: 65 Preprocesssing: Resize to 640x640 Patience: 50 Training Framework: Ultralytics Hardware: Google Colab with T4 GPU
| Class Name | Number of Annotations |
|---|---|
| Blue | 59 |
| Cipro 500 | 111 |
| Ibuphil 600 mg | 2 |
| Ibuphil Cold 400-60 | 71 |
| Pink | 58 |
| Red | 60 |
| White | 60 |
| Xyzall 5mg | 71 |
Evaluation Results
The model currently confuses many classes for the background and misses many classes.
Limitations and Biases
This model has several issues
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