Setting Up Zero-Shot Object Detection on Ubuntu using PyTorch

This guide provides step-by-step instructions to set up a zero-shot object detection system on Ubuntu using PyTorch. Zero-shot object detection allows models to detect objects that were not included in the training set.

1. Install System Prerequisites

First, update your Ubuntu system and install the necessary dependencies. Open a terminal and run the following commands:

sudo apt update
sudo apt upgrade
sudo apt install python3 python3-pip git

2. Install PyTorch and Required Libraries

Install PyTorch along with the other required libraries. You can follow the official installation instructions for your specific version or use the command below:

pip install torch torchvision transformers opencv-python matplotlib

3. Clone the Zero-Shot Object Detection Repository

For this setup, we will use a zero-shot object detection implementation based on Hugging Face's Transformers library. Clone the necessary repository:

git clone https://github.com/facebookresearch/detectron2.git
cd detectron2

4. Install Detectron2

Follow the installation instructions in the detectron2 repository. Typically, you will need to install the package using:

python3 -m pip install -e .

5. Prepare Your Input Data

Create a folder for your images. Place the images you want to analyze in this folder. For example:

mkdir data
cp /path/to/your/images/*.jpg data/

6. Create a Zero-Shot Object Detection Script

Create a new Python script called zero_shot_object_detection.py and add the following code:

import torch
import cv2
import matplotlib.pyplot as plt
from transformers import DetrImageProcessor, DetrForObjectDetection

# Load the processor and model
processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")

# Load and process the image
image_path = "data/your_image.jpg"  # Replace with your image path
image = cv2.imread(image_path)
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# Prepare the image for detection
inputs = processor(images=image_rgb, return_tensors="pt")

# Perform inference
with torch.no_grad():
    outputs = model(**inputs)

# Get the predicted boxes and labels
target_sizes = torch.tensor([image.shape[:2]])
results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]

# Visualize the results
plt.imshow(image_rgb)
ax = plt.gca()

for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
    if score > 0.9:  # Filter out low-confidence predictions
        box = [box[0].item(), box[1].item(), box[2].item(), box[3].item()]
        ax.add_patch(plt.Rectangle((box[0], box[1]), box[2] - box[0], box[3] - box[1], fill=False, color="red", linewidth=2))
        ax.text(box[0], box[1], f"{model.config.id2label[label.item()]}: {score:.2f}", fontsize=12, color="red")

plt.axis("off")
plt.show()

Make sure to replace data/your_image.jpg with the actual path to your image.

7. Run the Zero-Shot Object Detection Script

Execute the script to perform zero-shot object detection on your input image:

python3 zero_shot_object_detection.py

This command will display the input image with bounding boxes around detected objects along with their labels.

8. Troubleshooting

If you encounter any issues, consider the following:

  • Ensure that all libraries are correctly installed.
  • Check the path to your input image.
  • Make sure you have a working internet connection for model downloading.

9. Conclusion

You have successfully set up a zero-shot object detection system on Ubuntu using PyTorch and the Hugging Face Transformers library. This system can be enhanced by experimenting with different models and input data.