After combing through numerous notebooks I realize that people are using the Arcface approach a lot. Since this is my first time encountering Arcface I did some digging and got to understand some few concepts. However I can't quite follow the way it's being used in this competition. This is what I think and please do correct me if I am wrong;
\nWhy is the step 3 necessary?
", "messages": [ { "id": "1743241", "postDate": "04/02/2022 19:30:18", "content": "After combing through numerous notebooks I realize that people are using the Arcface approach a lot. Since this is my first time encountering Arcface I did some digging and got to understand some few concepts. However I can't quite follow the way it's being used in this competition. This is what I think and please do correct me if I am wrong;
\nWhy is the step 3 necessary?
", "rawMarkdown": "After combing through numerous notebooks I realize that people are using the Arcface approach a lot. Since this is my first time encountering Arcface I did some digging and got to understand some few concepts. However I can't quite follow the way it's being used in this competition. This is what I think and please do correct me if I am wrong;\n1. We use arcface to train and validate on the embeddings of the data rather than the final classification layer.\n2. The learned model will then be used to make a prediction of the training dataset and the test dataset to generate their embeddings respectively. These embeddings will be stored.\n3. Using some sort of KNN, we train again on the train embeddings. After which we will use the resulting model to predict on the test embeddings.\n\nWhy is the step 3 necessary?", "votes": null }, { "id": "1743296", "postDate": "04/02/2022 20:39:22", "content": "Not sure step 3 is actually what people are doing.
\nOnce we have embeddings, we just do something like the following: for each test embedding, find the nearest 5 train embeddings. These are our predictions. Based on the distance between these and our test embeddings, we may wish to make say that the whale is a new_individual, rather than one of our existing training examples.
\nMaybe you got confused because people use sklearn API which involves a train method on the embeddings, but it's just indexing for KNN.
", "rawMarkdown": "Not sure step 3 is actually what people are doing. \n\nOnce we have embeddings, we just do something like the following: for each test embedding, find the nearest 5 train embeddings. These are our predictions. Based on the distance between these and our test embeddings, we may wish to make say that the whale is a new_individual, rather than one of our existing training examples.\n\nMaybe you got confused because people use sklearn API which involves a train method on the embeddings, but it's just indexing for KNN.", "votes": null }, { "id": "1743630", "postDate": "04/03/2022 06:49:46", "content": "On step 3 is not actually a KNN, it is Nearest Neighbors. The sound the same, they even have a similar technique but there are not the same.
\nKNN uses a voting system for classification and mean (and other techniques) for a regression problem.
\nNearest neighbors calculates just distances in a n-dimensional space.
\nIn the competition context, it is used after getting the embeddings (output of the convolutional NN). Having the embeddings for all images, we want to know which images are similar (same individual) and we do that using the supposition that similar images are having similar embeddings. So, for a test image, after running it though the convolutional neural network and getting the embeddings our most likely predictions for who that individual is are the images that in the n-dimensional embeddings space are more close to the embeddings of the unknown image.
So, the flow is:
\nStep 1: Train a CNN at your choice which will output a number of embeddings (128, 256, 512, 1024, etc.). For training the convolutional to get closer embeddings for similar images and further apart embeddings for different images we are using the ArcFace
\nStep 2: Using the trained CNN, predict on the train and validation (here you don't need ArcFace, you just want to get the predicted embeddings, not to train). Use nearest neighbors technique to create a n-dimensional space for training data and find out the closest neighbor for valid embeddings.
\nCreate a cross-validation system for determining the right thresholds for certainty and for prediction under that level insert \"new individual\".
\nStep 3: Merge train and valid embeddings, create a n-dimensional space with nearest neighbors and find closest 5 neighbors for test data and then use the optimum confidence threshold found in cross validation to insert new individual
You should refer this discussion to have a good idea of what should be done.
\n\n\nWhy is the step 3 necessary?
\n
It is not. You could use every method you like for finding closest prediction, the simplest is np.dot(test_embeddings, train_embeddings) and sort line by line.
Not sure step 3 is actually what people are doing.
\nOnce we have embeddings, we just do something like the following: for each test embedding, find the nearest 5 train embeddings. These are our predictions. Based on the distance between these and our test embeddings, we may wish to make say that the whale is a new_individual, rather than one of our existing training examples.
\nMaybe you got confused because people use sklearn API which involves a train method on the embeddings, but it's just indexing for KNN.
", "votes": null, "replies": [] }, { "id": 1743630, "author_name": "vladvdv", "author_url": "", "post_date": "04/03/2022 06:49:46", "content": "On step 3 is not actually a KNN, it is Nearest Neighbors. The sound the same, they even have a similar technique but there are not the same.
\nKNN uses a voting system for classification and mean (and other techniques) for a regression problem.
\nNearest neighbors calculates just distances in a n-dimensional space.
\nIn the competition context, it is used after getting the embeddings (output of the convolutional NN). Having the embeddings for all images, we want to know which images are similar (same individual) and we do that using the supposition that similar images are having similar embeddings. So, for a test image, after running it though the convolutional neural network and getting the embeddings our most likely predictions for who that individual is are the images that in the n-dimensional embeddings space are more close to the embeddings of the unknown image.
So, the flow is:
\nStep 1: Train a CNN at your choice which will output a number of embeddings (128, 256, 512, 1024, etc.). For training the convolutional to get closer embeddings for similar images and further apart embeddings for different images we are using the ArcFace
\nStep 2: Using the trained CNN, predict on the train and validation (here you don't need ArcFace, you just want to get the predicted embeddings, not to train). Use nearest neighbors technique to create a n-dimensional space for training data and find out the closest neighbor for valid embeddings.
\nCreate a cross-validation system for determining the right thresholds for certainty and for prediction under that level insert \"new individual\".
\nStep 3: Merge train and valid embeddings, create a n-dimensional space with nearest neighbors and find closest 5 neighbors for test data and then use the optimum confidence threshold found in cross validation to insert new individual
You should refer this discussion to have a good idea of what should be done.
\n\n\nWhy is the step 3 necessary?
\n
It is not. You could use every method you like for finding closest prediction, the simplest is np.dot(test_embeddings, train_embeddings) and sort line by line.