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| make sense | yolov5.js |
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| Free to use online tool for labelling photos in small Computer Vision projects. | Effortless YOLOv5 JavaScript deployment. Enrich your website with Computer Vision. |
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| 190K (67K) | Nowadays, having at our disposal many high-level, specialized libraries and frameworks such as Keras, TensorFlow or PyTorch, we do not need to constantly worry about the size of our weights matrices or remembers formula for the derivative of activation function we decided to use. Often all we need to create a neural network, even one with a very complicated structure, is a few imports and a few lines of code. [...] |
| 247K (74K) | [...] Not so long ago I published an article, explaining — in a simple way — how neural nets work. However, it was highly theoretical post, dedicated primarily to math, which is the source of NN superpower. From the beginning I was planning to follow-up this topic in a more practical way. This time we will try to utilize our knowledge and build a fully operational neural network using only NumPy. [...] |
| 151K (48K) | Autonomous driving, healthcare or retail are just some of the areas where Computer Vision has allowed us to achieve things that, until recently, were considered impossible. Today the dream of a self driving car or automated grocery store does not sound so futuristic anymore. In fact, we are using Computer Vision every day — when we unlock the phone with our face or automatically retouch photos before posting them on social media. [...] |