AI

📦 Building a Custom Object Detection Model (Brand Detection) with YOLOv11 in Google Colab  Object detection has become significantly more accessible thanks to modern architectures like YOLO (You Only Look Once). This article walks through the entire process of setting up and training a YOLO model from scratch using custom image dataset — right inside Google Colab. 📁 1. Uploading and Preparing the Dataset ▶ Mount Google Drive (if your dataset is there) from google.colab import drive drive.mount('/content/gdrive') This gives Colab access to your Google Drive so it can copy your dataset stored there. ▶ Copy the dataset ZIP file from Google Drive !cp /content/gdrive/MyDrive/yolo/data.zip /content Copies the ZIP archive containing your dataset into the Colab runtime. ▶ Alternative: Download from a URL !wget -O /content/data.zip https://drive.google.com/file/d/1Hdrk5_dnJvJPoupPsJ0PaRyLPPDP7b4f/view?usp=sharing/data.zip Downloads the dataset from a public URL directly into the Col...

  Building a Handwritten Multi-Digit Recognition   Setting up the Local Environment Open a command prompt, created a folder digitapp and create a virtual environment mkdir digitapp cd digitapp python -m venv digitapp   Activate the virtual environment digitapp\Scripts\activate   Install the necessary libraries File: requirements.txt joblib==1.5.0 keras==3.8.0 matplotlib==3.10.0 numpy==2.0.2 pandas==2.2.2 scikit-learn==1.6.1 seaborn==0.13.2 streamlit==1.45.0 streamlit-drawable-canvas==0.9.3   # Command to execute for installation of the libraries pip install -r requirements.txt   Create a model File: trainer.py   #~~~ Working on the Data set ~~~   # 'Import necessary libraries' import numpy as np from tensorflow.keras import layers        # For defining layers of the neural network from tensorflow.keras import models      # For buildi...