darknet
tensorflow
| darknet | tensorflow | |
|---|---|---|
| 22 | 223 | |
| 25,292 | 182,456 | |
| - | 0.5% | |
| 0.0 | 10.0 | |
| about 1 month ago | 6 days ago | |
| C | C++ | |
| GNU General Public License v3.0 or later | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
darknet
- Llama.cpp: Full CUDA GPU Acceleration
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How to identify a senior developer
This reminds me of the resume for the guy who made darknet https://pjreddie.com/darknet/
- Anyone taking CS8803-O15: Computing Law?
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I can’t take this paper seriously anymore
Love the darknet (also made by him) github. Like what is this?
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YoloV7 Finally an official Yolo. This should actually be V5
I don’t know, the OG author seemed pretty lax on its use based on the license.
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I think this needs a post of its own
In that case you're sure to enjoy this one.
- Avoid negative output from yolo model
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Is there a functioning neural netowork or backbone written in pure C language only?
Literally the first google link dude… https://github.com/pjreddie/darknet
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Face Recognition
Election of tools: you should define if you are going to use machine/deep learning methods or classical approaches such as the Viola-Jones algorithm. I will recommend you to use ML/DL with TensorFlow (Object Detection API) or Darknet (YOLO).
- Show HN: An AI program to check videos for NSFW content
tensorflow
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Side Quest Devblog #1: These Fakes are getting Deep
# L2-normalize the encoding tensors image_encoding = tf.math.l2_normalize(image_encoding, axis=1) audio_encoding = tf.math.l2_normalize(audio_encoding, axis=1) # Find euclidean distance between image_encoding and audio_encoding # Essentially trying to detect if the face is saying the audio # Will return nan without the 1e-12 offset due to https://github.com/tensorflow/tensorflow/issues/12071 d = tf.norm((image_encoding - audio_encoding) + 1e-12, ord='euclidean', axis=1, keepdims=True) discriminator = keras.Model(inputs=[image_input, audio_input], outputs=[d], name="discriminator")
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Google lays off its Python team
[3]: https://github.com/tensorflow/tensorflow/graphs/contributors
- TensorFlow-metal on Apple Mac is junk for training
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🔥🚀 Top 10 Open-Source Must-Have Tools for Crafting Your Own Chatbot 🤖💬
To get up to speed with TensorFlow, check their quickstart Support TensorFlow on GitHub ⭐
- One .gitignore to rule them all
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10 Github repositories to achieve Python mastery
Explore here.
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GitHub and Developer Ecosystem Control
Part of the major userbase pull in GitHub revolves around hosting a considerable number of popular projects including Angular, React, Kubernetes, cpython, Ruby, tensorflow, and well even the software that powers this site Forem.
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Non-determinism in GPT-4 is caused by Sparse MoE
Right but that's not an inherent GPU determinism issue. It's a software issue.
https://github.com/tensorflow/tensorflow/issues/3103#issueco... is correct that it's not necessary, it's a choice.
Your line of reasoning appears to be "GPUs are inherently non-deterministic don't be quick to judge someone's code" which as far as I can tell is dead wrong.
Admittedly there are some cases and instructions that may result in non-determinism but they are inherently necessary. The author should thinking carefully before introducing non-determinism. There are many scenarios where it is irrelevant, but ultimately the issue we are discussing here isn't the GPU's fault.
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Can someone explain how keras code gets into the Tensorflow package?
and things like y = layers.ELU()(y) work as expected. I wanted to see a list of the available layers so I went to the Tensorflow GitHub repository and to the keras directory. There's a warning in that directory that says:
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Is it even possible to design a ML model without using Python or MATLAB? Like using C++, C or Java?
Exactly what language do you think TensorFlow is written in? :)
What are some alternatives?
yolov7 - Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
PaddlePaddle - PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署）
ncnn - ncnn is a high-performance neural network inference framework optimized for the mobile platform
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
darknet - YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet )
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
labelImg - LabelImg is now part of the Label Studio community. The popular image annotation tool created by Tzutalin is no longer actively being developed, but you can check out Label Studio, the open source data labeling tool for images, text, hypertext, audio, video and time-series data.
LightGBM - A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
pytorch_nsfw_model - Pytorch model for NSFW classification with usage example
scikit-learn - scikit-learn: machine learning in Python
deepC - vendor independent TinyML deep learning library, compiler and inference framework microcomputers and micro-controllers
LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.