replicate-javascript VS scikit-image

We used Replicate to run the models and the cost so far is 26 cents for 90 cards — which means it’s less than a third of a cent per card!

Basically https://replicate.com/

Because it happens when running your own models on localhost too. I have ollama and all the ones they support, but there are some on HuggingFace I run through llama.cpp inside apps where I won't have ollama installed, Replicate also has Stable Diffusion models, not just chat ones, and OpenAI which is its own thing. So it could potentially all be unified under a provider like that.

Haven't actually tried Replicate because I'm just running locally for free, but probably would try to find a single cloud provider for all deployments, like a Heroku of LLMs.

It's very easy to get started, right in your Terminal, no fees! No credit card at all.

And there are cloud providers like https://replicate.com/ and https://lightning.ai/ that will let you use your LLM via an API key just like you did with OpenAI if you need that.

You don't need OpenAI - nobody does.

In this section, we’ll show you how to generate MDE depth map predictions with both DPT and Marigold. In both cases, you can optionally run the model locally with the respective Hugging Face library, or run remotely with Replicate.

Built with Supabase, Astro, Unreal Speech, Stable Diffusion, Replicate, Metropolitan Museum of Art

Now that we understand the process, let’s take a look at the actual code. The first step is simply importing our dependencies. Note that we will be using the replicate npm package, which you can install with npm i replicate.

We will use the Hugging Face transformers and diffusers libraries for inference, FiftyOne for data management and visualization, and scikit-image for evaluation metrics.

Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks.

scikit hog library: https://github.com/scikit-image/scikit-image/blob/main/skimage/feature/_hog.py#L302 , https://github.com/scikit-image/scikit-image/blob/main/skimage/feature/_hoghistogram.pyx

This is a good cv deep learning book with python examples https://www.manning.com/books/deep-learning-for-vision-systems. If you're pretty comfortable with the concepts of traditional image processing this is a good companion to cv2 (so you don't have to reinvent the wheel) https://scikit-image.org/

Also, don't know if you're familiar with Python, but if you need ideas for to implement for future directions : https://scikit-image.org/

There's probably something in scikit-image to do what you want, or close enough to build on.

Scikit-image The Scikit-image library is a collection of image processing algorithms that are designed to be easy to use and understand. It includes algorithms for common tasks like edge detection, feature extraction, and image restoration. If you are just starting out in image processing, then this is a good library to check out!

Python is a general-purpose programming language that provides many image processing libraries for adding image processing capabilities to digital images. Some of the most common image processing libraries in Python are OpenCV, Python Imaging Library (PIL), Scikit-image etc.

Then you would need to find what this plugin does for imshow. First thing you can see is that "interpolation" is not "bicubic" as you used, but "nearest"… but there are other settings here that are responsible for the difference of displays. (it's better that you look at the source code in your environment, as it might be slightly different)