Object Detection with Hugging Face (original) (raw)

Last Updated : 28 Mar, 2026

Object detection using models from Hugging Face enables developers to identify and locate objects within images by drawing bounding boxes and assigning labels. With pretrained transformer and vision models, it becomes easy to build computer vision applications without training models from scratch.

object_detection_models_and_their_real_world_applications

Object Detection

For example, in a street image, an object detection system can detect cars, traffic lights, pedestrians

Implementation of Object Detection

**Step 1: Set Up the Environment

First, install the required libraries. Run the following command in your command prompt.

pip install transformers torch pillow matplotlib

**Step 2: Import Required Libraries

Python `

from transformers import pipeline from PIL import Image import matplotlib.pyplot as plt import matplotlib.patches as patches

**Step 3: Initialize Object Detection Pipeline

This code initializes an object detection model using the pipeline API.

**task="object-detection" : Specifies that we want to perform object detection on images.
**model="facebook/detr-resnet-50" : Loads a pre trained DETR model (Detection Transformer) with a ResNet-50 backbone, trained to detect common objects. Python `

detector = pipeline( task="object-detection", model="facebook/detr-resnet-50" )

**Output:

hugging_face_pretrained_model

Loading pretrained model

**Step 4: Load and Display Image

This opens the image file and displays it using Matplotlib, confirming that the image has been loaded correctly before running object detection.

You can download the image from here

Python `

image = Image.open("your image path")

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

**Output:

Object-detection

Image

**Step 5: Run Detection

Each detected object includes:

**label: Predicted object class
**score: Confidence score
**box: Bounding box coordinates (xmin, ymin, xmax, ymax)

These results show what objects were detected and where they are located in the image.

Python `

results = detector(image)

for obj in results: print(obj)

**Output:

Output_huggingface4

Output

**Step 6: Visualize Bounding Boxes Properly

This code draws bounding boxes around each detected object and displays the label with its confidence score directly on the image.

Clearly visualizes detected objects
Handles detection results in a structured way
Clean and practical for real world applications Python `

fig, ax = plt.subplots(1) ax.imshow(image)

for obj in results: box = obj["box"] xmin, ymin = box["xmin"], box["ymin"] xmax, ymax = box["xmax"], box["ymax"]

width = xmax - xmin
height = ymax - ymin

rect = patches.Rectangle(
    (xmin, ymin),
    width,
    height,
    linewidth=2,
    edgecolor="red",
    facecolor="none"
)

ax.add_patch(rect)
ax.text(
    xmin,
    ymin,
    f"{obj['label']} ({obj['score']:.2f})",
    color="yellow",
    fontsize=10,
    backgroundcolor="black"
)

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

**Output:

Detected_objects

Detected Objects

You can download the full code from here