bpycv
pytorch-CycleGAN-and-pix2pix
| bpycv | pytorch-CycleGAN-and-pix2pix | |
|---|---|---|
| 3 | 10 | |
| 455 | 21,998 | |
| - | - | |
| 4.6 | 2.8 | |
| about 2 months ago | 8 days ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 or later |
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bpycv
- Bpycv: Computer Vision and Deep Learning Utils for Blender
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Python code that takes pictures from different view points of a 3d model
Blender is capable of running in a headless mode. I ‘ve had the exact same use case nearly a thousand times. This library worked out well for my purposes https://github.com/DIYer22/bpycv
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[P] Synthetic Data for CV with Python and Blender
Can someone who knows a bit about this compare to, e.g., bpycv? Seems like they are doing very similar stuff
pytorch-CycleGAN-and-pix2pix
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List of AI-Models
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- I want an A.I. to learn my art style so I can keep making art in my art style despite not having the time to do it.
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I'm looking for an AI Art generator from images
pix2pix (https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) - This is a PyTorch implementation of the pix2pix algorithm for image-to-image translation. Given a set of images, the model can learn to generate a new image from a different domain that is similar to the input image.
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Seamless textures with SD and PBR maps with a pix2pix cGAN
Using junyanz/pytorch-CycleGAN-and-pix2pix as a basis for pix2pix, I applied the same blending method to fix seams. It essentially takes an input image and generates an output. The results depend on the paired training data. In this case, each map (height, roughness, etc.) is a separate checkpoint and had to be trained on paired training data with the diffuse as the input and the respective map as the output.
- IA art
- Segmentation and clasification with UNET
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Trying to understand PatchGAN discriminator
Code for https://arxiv.org/abs/1611.07004 found: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
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I made a 3d topographic map based on my recent civ6 game
pix2pix algorithm is used for translating Civ6Maps to heightmaps. Synthesized terrain was rendered in blender.
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This Wojak Does Not Exist
https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
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Training a neural net to generate Wojaks
I'm working on creating a face-to-wojak model using PyTorch CycleGan/Pix2Pix [0] and found some of my outputs to be outrageous yet somehow relatable. People are into it so thought I'd share on HN
[0] https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
What are some alternatives?
labelme2coco - A lightweight package for converting your labelme annotations into COCO object detection format.
pix2pixHD - Synthesizing and manipulating 2048x1024 images with conditional GANs
zpy - Synthetic data for computer vision. An open source toolkit using Blender and Python.
generative-inpainting-pytorch - A PyTorch reimplementation for paper Generative Image Inpainting with Contextual Attention (https://arxiv.org/abs/1801.07892)
labelme - Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation).
pytorch-grad-cam - Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
sahi - Framework agnostic sliced/tiled inference + interactive ui + error analysis plots
Deep-Fakes
learning-topology-synthetic-data - Tensorflow implementation of Learning Topology from Synthetic Data for Unsupervised Depth Completion (RAL 2021 & ICRA 2021)
AnimeGAN - Generating Anime Images by Implementing Deep Convolutional Generative Adversarial Networks paper
pyrender - Easy-to-use glTF 2.0-compliant OpenGL renderer for visualization of 3D scenes.
PaddleGAN - PaddlePaddle GAN library, including lots of interesting applications like First-Order motion transfer, Wav2Lip, picture repair, image editing, photo2cartoon, image style transfer, GPEN, and so on.