glasses
gan-vae-pretrained-pytorch
| glasses | gan-vae-pretrained-pytorch | |
|---|---|---|
| 2 | 1 | |
| 413 | 162 | |
| - | - | |
| 1.8 | 0.0 | |
| over 1 year ago | over 2 years ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | - |
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glasses
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Are Open-sourced Implementations Sometimes Over-engineered?
Yes, they are. Take with a grain of salt, but researchers (usually) do not know how to code and (or) they don't care to properly share their work. Things that are learned in the first Computer Science bachelor year, like OOP, DRY, packages, good variables/function naming, are apparently not used in ml research. This is why I created my own library (https://github.com/FrancescoSaverioZuppichini/glasses), for me, good code means less time I have to spend working and more free time.
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[N] Facebook announced a new AI open-source called DeiT (A new technique to train computer vision models)
I have implemented most of the sota models in my library (https://github.com/FrancescoSaverioZuppichini/glasses). These are my 2 cents:
gan-vae-pretrained-pytorch
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DCGAN (CIFAR-10) Generating fake images is easy, but how to also output the class label (1 to 10) with the fake generated images?
I have this DCGAN model (https://github.com/csinva/gan-vae-pretrained-pytorch/tree/master/cifar10_dcgan) which generates fake Cifar-10 images. However I also want to get the intended class label output with the fake generated images. How can I do this? This model which I found only generates fake images but doesn't know what class the generated images belong to.
What are some alternatives?
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
AvatarGAN - Generate Cartoon Images using Generative Adversarial Network
monodepth2 - [ICCV 2019] Monocular depth estimation from a single image
pytorch-GAT - My implementation of the original GAT paper (Veličković et al.). I've additionally included the playground.py file for visualizing the Cora dataset, GAT embeddings, an attention mechanism, and entropy histograms. I've supported both Cora (transductive) and PPI (inductive) examples!
conformal_classification - Wrapper for a PyTorch classifier which allows it to output prediction sets. The sets are theoretically guaranteed to contain the true class with high probability (via conformal prediction).
AnimeGAN - Generating Anime Images by Implementing Deep Convolutional Generative Adversarial Networks paper
One-Piece-Image-Classifier - A quick image classifier trained with manually selected One Piece images.
AI-For-Beginners - 12 Weeks, 24 Lessons, AI for All!
neuralforecast - Scalable and user friendly neural :brain: forecasting algorithms.
Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning - My Computer Vision project from my Computer Vision Course (Fall 2020) at Goethe University Frankfurt, Germany. Performance comparison between state-of-the-art Object Detection algorithms YOLO and Faster R-CNN based on the Berkeley DeepDrive (BDD100K) Dataset.
HugsVision - HugsVision is a easy to use huggingface wrapper for state-of-the-art computer vision
dnn_from_scratch - A high level deep learning library for Convolutional Neural Networks,GANs and more, made from scratch(numpy/cupy implementation).