Closed Saksh8 closed 6 months ago
Ok explain the plan like dataset and further approaches. I shall then assign to you.
Dataset is on Kaggle containing 927 image files labeled using Roboflow in YOLOv8 format. This dataset was divided into train, validation, and test sets, and data replication processes were applied.
To implement my model, I decided to base it on the YOLOv8 architecture, drawing inspiration from the Keras example. This architecture is well-suited for object detection tasks like identifying acne in images.
One of the key components of my approach was leveraging TensorFlow's tf.data
API for building an efficient input pipeline. This allowed me to preprocess the dataset and create batches of training data seamlessly, optimizing the training process.
Since the dataset was labeled in YOLOv8 format, I crafted a parser function to read and interpret the bounding box annotations and class labels from the accompanying text files.
With my input pipeline ready and the YOLOv8 model architecture defined, I embarked on the training phase. I monitored the training process closely, using the validation data to gauge the model's performance and make adjustments as needed.
After training, I will evaluate the model's performance on the test data to assess its accuracy and generalization capabilities.
@Saksh8 Perfect, let's proceed in steps. Each step has its separate issue and thus separate points.
Input pipeline and Dataset preprocessing -- noise removal, augmentation, etc. -> Level 2 (continue this issue only) Next take up different models like custom CNN, Dilated CNN, Pre-trained models, Graph CNN, etc. We will have separate issue for each type of model.
Make sure to add proper readme in separate folder for Acne.
Sure.Thank You
I have resolved this issue also .
I have resolved this issue also .
ok will have a look today.
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I wish to add a deep learning model that could predict predict acne based on the image analysis