Very Deep Convolutional Networks for Large-Scale Image Recognition Article Swipe
In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.
Very deep within the layers of our dreams,
we stitch together visions, bright and bold,
where networks pulse, alive with unseen schemes.
In the heart of each convolution lies a story told.
A grand architecture, rising high, like art,
shaping the landscapes of thought and sight,
every pixel a piece, every filter a part,
we redefine recognition, bringing darkness to light.
In this vast sea of images we find,
patterns emerging from chaos, we embrace,
with every weight lifted, we expand the mind,
transforming the world, every moment we trace.
Here, in this dance of data and design,
the essence of creation, pure and free,
let's celebrate the spirit, the spark, the sign,
of networks that bind us, endlessly, very.
