alankbi

# Specify the path to your image
image = utils.read_image('images/image0.jpg')
predictions = model.predict(image)

# predictions format: (labels, boxes, scores)
labels, boxes, scores = predictions

print(labels) 
print(boxes)
print(scores)

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from detecto import core

# All images and XML files in the same folder
dataset = core.Dataset('images/')

# Images and XML files in separate folders
dataset = core.Dataset('labels/', 'images/')

image, target = dataset[0]
print(image, target)

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import matplotlib.pyplot as plt

val_dataset = core.Dataset('validation_images/')

losses = model.fit(loader, val_dataset, epochs=10, learning_rate=0.001, 
                   lr_step_size=5, verbose=True)
                   
plt.plot(losses)
plt.show()

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model.save('model_weights.pth')

# ... Later ...

model = core.Model.load('model_weights.pth', ['alien', 'bat', 'witch'])

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from torchvision import transforms

augmentations = transforms.Compose([
    transforms.ToPILImage(),
    transforms.RandomHorizontalFlip(0.5),
    transforms.ColorJitter(saturation=0.5),
    transforms.ToTensor(),
    utils.normalize_transform(),
])

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visualize.detect_video(model, 'input.mp4', 'output.avi')

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visualize.show_labeled_image(image, boxes, labels)

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# Specify the path to your image
image = utils.read_image('images/image0.jpg')
predictions = model.predict(image)

# predictions format: (labels, boxes, scores)
labels, boxes, scores = predictions

# ['alien', 'bat', 'bat']
print(labels) 

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from detecto import core, utils, visualize

dataset = core.Dataset('images/')
model = core.Model(['alien', 'bat', 'witch'])

model.fit(dataset)

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import os
from google.colab import drive

drive.mount('/content/drive')

os.chdir('/content/drive/My Drive/Detecto Tutorial')

!pip install detecto