How can classical ML be used to improve product recognition?

Classical machine learning (ML) methods can be effectively used to improve product recognition, especially when data volume is limited or interpretability of the model is required.

Main steps:

1. Data collection and preparation — gather product images and related information, annotate the data.
2. Feature extraction — use image processing methods (e.g., histograms, SIFT, HOG) or pre-trained neural networks to obtain features.
3. Model selection — classical algorithms such as SVM, random forest, logistic regression can be applied for product classification based on extracted features.
4. Training and validation — train the model on training data and evaluate its quality on test data.
5. Optimization and integration — tune hyperparameters, integrate the model into the recognition system.

Example in Python using SVM and HOG features:

from skimage.feature import hog
from sklearn import svm
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import cv2
import numpy as np

# Loading and preparing images
images = [...]  # list of product images
labels = [...]  # corresponding labels

# Extracting HOG features
features = [hog(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), pixels_per_cell=(8,8)) for img in images]

# Splitting data
X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2)

# Training SVM
clf = svm.SVC(kernel='linear')
clf.fit(X_train, y_train)

# Evaluation
predictions = clf.predict(X_test)
print("Accuracy:", accuracy_score(y_test, predictions))

This approach allows improving product recognition without complex neural networks, using classical algorithms and image processing methods.