Aan de slagGa gratis aan de slag

Object detection

In this exercise, you will use the same flickr dataset as previously, which has 30,000 images and associated captions. Now you will find bounding boxes of objects detected by the model.

Photo of 2 people, 1 is playing the guitar

The sample image (image) and pipeline module (pipeline) have been loaded.

Deze oefening maakt deel uit van de cursus

Multi-Modal Models with Hugging Face

Cursus bekijken

Oefeninstructies

  • Load the object-detection pipeline with facebook/detr-resnet-50 pretrained model.
  • Find the label of the detected object.
  • Find the associated confidence score of the detected object.
  • Find the bounding box coordinates of the detected object.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Load the object-detection pipeline
pipe = pipeline("____", "____", revision="no_timm")
pred = pipe(image)
outputs = pipe(image)

for n, obj in enumerate(outputs):
    # Find the detected label
    label = ____
    # Find the confidence score of the prediction
    confidence = ____
    # Obtain the bounding box coordinates
    box = ____
    
    plot_args = {"linewidth": 1, "edgecolor": colors[n], "facecolor": 'none'}
    rect = patches.Rectangle((box['xmin'], box['ymin']), box['xmax']-box['xmin'], box['ymax']-box['ymin'], **plot_args)
    ax.add_patch(rect)
    print(f"Detected {label} with confidence {confidence:.2f} at ({box['xmin']}, {box['ymin']}) to ({box['xmax']}, {box['ymax']})")

plt.show()
Code bewerken en uitvoeren