ComboLoss
jina
ComboLoss | jina | |
---|---|---|
1 | 126 | |
30 | 20,041 | |
- | 1.0% | |
3.6 | 9.1 | |
over 3 years ago | 13 days ago | |
Python | Python | |
MIT License | Apache License 2.0 |
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ComboLoss
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[D] Could this network be used to generate the most attractive image possible? What would it look like... -"ComboLoss for Facial Attractiveness Analysis with Squeeze-and-Excitation Networks"
Abstract: Loss function is crucial for model training and feature representation learning, conventional models usually regard facial attractiveness recognition task as a regression problem, and adopt MSE loss or Huber variant loss as supervision to train a deep convolutional neural network (CNN) to predict facial attractiveness score. Little work has been done to systematically compare the performance of diverse loss functions. In this paper, we firstly systematically analyze model performance under diverse loss functions. Then a novel loss function named ComboLoss is proposed to guide the SEResNeXt50 network. The proposed method achieves state-of-the-art performance on SCUT-FBP, HotOrNot and SCUT-FBP5500 datasets with an improvement of 1.13%, 2.1% and 0.57% compared with prior arts, respectively. Code and models are available at this https URL.
jina
- Jina.ai: Self-host Multimodal models
- FLaNK Stack Weekly for 30 Oct 2023
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Cross data type search that wasn’t supported well using Elasticsearch
Jina mainly because of their use of neural networks and AI.
- Recommend a Lightweight Launcher with Nested Folders
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I plan to build my own AI powered search engine for my portfolio. Do you know ones that are open-source?
Jina - It’s an open-source project where you can build search engines. Well maybe not no code but it claims that you only need a few lines of code for creating projects. The project supports semantic, text, image, audio, and video search. What I’m also interested in is with their neural search and generative AI. I’m also interested in the amount of github repo that they have. I have this on my radar since this is also something I was interested in.
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How can we match images in our database?
Do you guys have any ideas how we can match images on our database? We’re working on a project that about matching images on our database. We were trying to use SIFT and some other similar methods, but for some reason, nothing doesn’t seem to be working that well. Does anyone have any suggestions for the most effective way to do this? Maybe some open-source solutions like HuggingFace or Jina AI? We just want to make sure our image matching is correct and that part’s been a bit of a struggle on our part.
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Can AI 3D model search engines be a thing this year?
The tech lets you find 3D models without sifting through tons of text - An information retrieval framework does the heavy lifting and compares models to each other, no descriptions or keywords needed.
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Any MLOps platform you use?
Jina AI -They offer a neural search solution that can help build smarter, more efficient search engines. They also have a list of cool github repos that you can check out. Similar to Vertex AI, they have image classification tools, NLPs, fine tuners etc.
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This week(s) in DocArray
Well, it's not exactly a new feature, but we've been working on early support for DocArray v2 in Jina.
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Multi-model serving options
Jina let’s you serve all of your models through the same Gateway while deploying them as individual microservices. You can also tie your models together in a pipeline if needed. Also some nice ML focussed features such as dynamic batching.
What are some alternatives?
pytorch-metric-learning - The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.
Weaviate - Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database.
d2l-en - Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
haystack - :mag: LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.
pix2pixHD - Synthesizing and manipulating 2048x1024 images with conditional GANs
dalle-flow - 🌊 A Human-in-the-Loop workflow for creating HD images from text
whoogle-search - A self-hosted, ad-free, privacy-respecting metasearch engine
es-clip-image-search - Sample implementation of natural language image search with OpenAI's CLIP and Elasticsearch or Opensearch.
growthbook - Open Source Feature Flagging and A/B Testing Platform
searxng - SearXNG is a free internet metasearch engine which aggregates results from various search services and databases. Users are neither tracked nor profiled.
jina-hub - An open-registry for hosting Jina executors via container images
astrofox - Astrofox is a motion graphics program that lets you turn audio into amazing videos.