Object Detection Project in Google Colab

Step 1: Install Required Libraries

PyTorch and torchvision are used for model loading and processing

Matplotlib is used for visualization

OpenCV and PIL are used for image handling

!pip install torch torchvision matplotlib opencv-python

Step 2: Import Required Libraries

import torch # Core PyTorch library for tensor operations and model inference from torchvision.models.detection import fasterrcnn_resnet50_fpn # Pre-trained Faster R-CNN model from torchvision.transforms import functional as F # Image transformation utilities from torchvision.utils import draw_bounding_boxes # For drawing bounding boxes from torchvision.transforms import ToPILImage # Convert tensor to PIL image import matplotlib.pyplot as plt # Visualization library from PIL import Image # Image processing library import cv2 # OpenCV for image handling import numpy as np # Numerical computations

Step 3: Load Pre-Trained Model

Faster R-CNN pre-trained on COCO dataset is loaded

The model is set to evaluation mode to avoid training-related computations

model = fasterrcnn_resnet50_fpn(pretrained=True) model.eval() # Set model to evaluation mode

Step 4: Upload and Test an Image

Google Colab provides a file upload feature to easily upload images

from google.colab import files # Colab-specific file upload library uploaded = files.upload() # Upload an image file image_path = list(uploaded.keys())[0] # Get the uploaded file name image = Image.open(image_path).convert("RGB") # Open and convert image to RGB

Convert image to PyTorch tensor

image_tensor = F.to_tensor(image) # Transform image to tensor

Step 5: Perform Object Detection

Perform inference using the pre-trained model

with torch.no_grad(): # Disable gradient computation for inference predictions = model([image_tensor])

Extract bounding boxes, labels, and scores from the model's output

boxes = predictions[0]['boxes'] # Bounding box coordinates labels = predictions[0]['labels'] # Detected class labels scores = predictions[0]['scores'] # Confidence scores

Step 6: Visualize the Results

Filter detections with confidence scores above a threshold

threshold = 0.5 # Confidence threshold for filtering keep = scores > threshold # Boolean mask for valid detections

Draw bounding boxes with labels on the image

image_with_boxes = draw_bounding_boxes( (image_tensor * 255).byte(), # Convert tensor to byte scale for visualization boxes[keep], # Filtered bounding boxes colors="red", # Bounding box color labels=[f"{label.item()}" for label in labels[keep]], # Class labels for boxes width=3 # Line width of bounding boxes )

Convert tensor back to PIL image for display

plt.figure(figsize=(12, 8)) # Set figure size plt.imshow(ToPILImage()(image_with_boxes)) # Display the image with boxes plt.axis("off") # Remove axes for cleaner display plt.show() # Show the visualization

Step 7: Save the Results

Save the processed image to the Colab environment

output_path = "detected_image.jpg" # Output file name image_with_boxes_pil = ToPILImage()(image_with_boxes) # Convert to PIL image image_with_boxes_pil.save(output_path) # Save image locally print(f"Detected image saved at {output_path}") # Confirm save location

Allow user to download the result