Being part of the perception team at an Autonomous Vehicle research lab I had been working on the development of the lane detection and tracking module for the vehicle catering to Indian road scenario. What made the project challenging were many factors unique to the Indian landscape like highly weathered lanes and unusually congested traffic problems which took the project close to 8 months to complete.The framework is trained and evaluated on the data collected by our experimental vehicle on Indian roads. The dataset consists of a total of 4500 frames with varying driving scenarios, including highways, urban roads, traffic, shadowed lanes, partially visible lanes and curved lanes.

Project Page/Github link : https://github.com/ayush1997/Robust-Lane-Detection-and-Tracking

Hi everyone,

I’m building a platform for deploying machine learning in production. It takes exported models (TensorFlow, PyTorch, XGBoost, etc), deploys them as web APIs, and handles things like autoscaling, log streaming, and inference on CPUs or GPUs. It’s open source and designed to be self-hosted on AWS (GitHub / website).

I’d love to hear your feedback!

Hello,

It’s my pleasure to share our recent work on Video Domain Adaptation with you!

We proposed large-scale cross-domain action datasets, and developed an attention-based spatio-temporal DA mechanism to achieve effective domain alignment.

​

Temporal Attentive Alignment for Large-Scale Video Domain Adaptation (ICCV 2019 Oral)

[GitHub] https://github.com/cmhungsteve/TA3N

[arXiv] https://arxiv.org/abs/1907.12743

​

Feel free to share with others 🙂

Hi guys, I have what could be a stupid question, but I see that I’m encountering this issue regularly and would like to know your opinion: so yesterday I was trying to improve my ML model in order to improve its accuracy, and found out that it was performing worse. Why? I checked the previous model architecture (saved with Keras plot_model) and saw what I did differently last week. No problem, I will just revert to that architecture and test again. Model overfits in half the epochs now. Damn, I also changed the dataset augmentation pipeline, now I cannot recreate those specific scores.

​

Basically this is my issue, I happen to develop a model for n-days, test it, save it etc. then after a couple of weeks I try to revert to “that good model setup I was having” and I cannot get the same results anymore as I changed too much stuff. I marginally fixed it by saving the model architecture as png using Keras in order to have a quick visual comparison, It’s not the end of the world, but I don’t have a clean way to deal with this issue. How do you guys avoid such problems?

​

Thank you!

Say I was using Bert and trained it on Squad 2.0 (which I have done) and came across:

Question: What is your favorite color?

Possible (Correct) Answer given:Blue

Possible (Wrong) Answer given:Lasagna

The idea would be that a model would predict Blue on the first one and nothing on the second one (implying it was not a feasible answer).

Is there any research or ideas on how (if possible) you could train a model like a Bert to do that? I feel like it should be doable however my current results with training on Squad 2.0 were not extremely promising so I’m not sure if I’m not thinking about it problem correct or if there is some research out there on how to better approach this.

It seems like a lot of high-impact machine learning research has recently involved work in image or text domains (or perhaps this is mostly what I’m exposed to because I read a lot of deep network papers). Are people here familiar with high-impact work/papers outside of these domains? Do you think the work in these areas is actually less frequent or impactful, or is that perhaps a perceptual bias due to the popularity of things like deep network approaches?

DeepMind, the artificial-intelligence company owned by Google parent Alphabet Inc., saw its revenue almost double last year, but gains were dwarfed by losses that increased to hundreds of millions of dollars.

The London-based company also has more than a billion dollars of debt due for repayment this year, according to full-year accounts for the year ended Dec. 31 posted to U.K. business registry Companies House.

Losses for 2018 widened to 470.2 million pounds ($572 million) from 302.2 million pounds in 2017. Revenue rose to 102.8 million pounds, up from 54.4 million pounds. Staff costs also nearly doubled against the year-ago period to 398 million pounds in 2018.

A debt of 1.04 billion pounds due this year includes an 883 million-pound loan from its owner. DeepMind had written assurances it would be financially supported for at least another year.

“Our DeepMind for Google team continues to make great strides bringing our expertise and knowledge to real-world challenges at Google scale, nearly doubling revenue in the past year,” a spokeswoman for the company said in a statement. “We will continue to invest in fundamental research and our world-class, interdisciplinary team, and look forward to the breakthroughs that lie ahead.”

Alphabet Inc. bought DeepMind for 400 million pounds in 2014. The next year, the company began working on health-care research, eventually creating an entire division dedicated to the area.

The company works with the U.K. National Health Service hospitals, researching algorithms that can diagnose eye diseases and spot head and neck cancers from medical imagery, and the U.S. Department of Veterans Affairs on an algorithm that can predict which patients are at risk of sudden deterioration from acute kidney injury and other conditions.

​

https://www.bloomberg.com/news/articles/2019-08-07/alphabet-s-deepmind-takes-on-billion-dollar-debt-as-loss-spirals

Abstract: Drones or general Unmanned Aerial Vehicles (UAVs), endowed with computer vision function by onboard cameras and embedded systems, have become popular in a wide range of applications. However, real-time scene parsing through object detection running on a UAV platform is very challenging, due to limited memory and computing power of embedded devices. To deal with these challenges, in this paper we propose to learn efficient deep object detectors through channel pruning of convolutional layers. To this end, we enforce channel-level sparsity of convolutional layers by imposing L1 regularization on channel scaling factors and prune less informative feature channels to obtain “slim” object detectors. Based on such approach, we present SlimYOLOv3 with fewer trainable parameters and floating point operations (FLOPs) in comparison of original YOLOv3 (Joseph Redmon et al., 2018) as a promising solution for real-time object detection on UAVs. We evaluate SlimYOLOv3 on VisDrone2018-Det benchmark dataset; compelling results are achieved by SlimYOLOv3 in comparison of unpruned counterpart, including ~90.8% decrease of FLOPs, ~92.0% decline of parameter size, running ~2 times faster and comparable detection accuracy as YOLOv3. Experimental results with different pruning ratios consistently verify that proposed SlimYOLOv3 with narrower structure are more efficient, faster and better than YOLOv3, and thus are more suitable for real-time object detection on UAVs

https://medium.com/@cdossman/slimyolov3-narrower-faster-and-better-for-real-time-uav-applications-ad3c5b8a2cf9