March 2021 Summaries

Brad Dwyer's blog post from March 30, 2021, outlines the process of using a USB webcam with a custom-trained Roboflow model for real-time image inference using Python. The tutorial requires Python 3.7+, along with installing several dependencies such as OpenCV, NumPy, and requests to connect the webcam, process the image data, and communicate with the Roboflow model API. The core of the demonstration involves capturing images from the webcam, resizing them, converting them to base64, and sending these to the Roboflow API for predictions, which are then displayed on the screen. The script runs in a loop until interrupted by the user pressing the 'q' key, and Dwyer suggests that performance can be improved by parallelizing requests to increase the frame rate. The accompanying code is available in the Roboflow API Snippets GitHub repository for those interested in experimenting with or enhancing the setup.

Content moderation is a complex but essential task for platforms that host user-generated content, as demonstrated by the example of Facebook's efforts to combat vaccine misinformation. Traditional moderation methods include human, non-model-based, and model-based approaches, each with unique advantages and challenges. While human moderation offers tailored oversight, it struggles with scalability, whereas automated methods can process large volumes of content but may inherit biases from training data. Enter OpenAI's CLIP model, which offers a novel "zero-shot" approach by understanding the semantic meaning of images without needing a pre-curated dataset. This capability was leveraged in the game paint.wtf, where CLIP evaluates drawings based on their alignment with given prompts using cosine similarity. The model can also identify inappropriate content, such as NSFW images, by comparing them against specific categories, thus reducing the burden of manual moderation and enhancing user experience. This innovative use of CLIP demonstrates its versatility in content moderation across unique domains, underscoring its potential to reshape how platforms manage user contributions.

Andrew Ng, a prominent figure in AI, discussed the shift from "big data" to "good data" at the Scale Transform conference, emphasizing the iterative nature of machine learning projects. He highlighted that many enterprise AI projects fail due to a lack of understanding of AI as a continuous feedback loop, where real-world data is crucial for improving models over time. Ng pointed out that data quality, not just quantity, is essential for AI models, comparing it to a balanced diet, and stressed that data cleaning occupies a significant portion of a machine learning engineer's work. He advised focusing on data improvements over tweaking model architectures, as the former often yields faster performance gains. Ng also underlined the importance of systematic and repeatable processes over ad-hoc methods like using Jupyter notebooks, advocating for production-grade tooling to ensure reliable AI systems. Platforms like Roboflow are designed to support this iterative process, helping organizations build proof of concept models, instill robust data practices, and continually enhance their projects with active learning.

Matt Brems announces a webinar co-hosted by Roboflow and Paperspace, scheduled for March 25, 2021, at 1:00 pm Eastern time, focused on building a Detectron2 object detection model using their platforms. The event will feature ML Engineers Jacob Solawetz from Roboflow and Rachel Rapp from Paperspace, who will guide participants through accessing and using public image datasets with Roboflow, preprocessing and augmenting images, and building a Detectron model with GPU-backed notebooks on Paperspace Gradient. Participants are encouraged to register and create accounts on both platforms in advance to follow along with the session. The webinar aims to demonstrate the seamless integration of Roboflow and Paperspace tools for computer vision applications, and those interested in further collaborations can explore additional resources and training sessions between the two companies.

Herberto Warner proposes a low-cost danger monitoring system for cyclists using computer vision and object detection to enhance safety on the streets. The prototype involves a Raspberry Pi 4, a camera, and an LED traffic light system that communicates danger levels to cyclists by detecting cars and bicycles. The project utilized three datasets, including real-life images captured from a cyclist's perspective and pre-labeled images from Google Open Images, to train the system. The data was preprocessed using Roboflow, which facilitated the creation of TFRecords and accelerated the development process. Employing the MobileNetSSD model for its fast inference time, the system evaluates danger based on object distance and size, with the LED light signaling different danger levels. The model excelled in detecting larger objects like cars but struggled with smaller objects such as bicycles, prompting future improvements in data collection and model exploration. Warner envisions the system's potential integration into smart cities to promote safer urban cycling environments.

Embedded machine learning involves deploying machine learning algorithms on microcontrollers, allowing them to run on small devices like Raspberry Pi, NVIDIA Jetson, Intel Movidius, and Luxonis OAK, rather than relying on powerful supercomputers or cloud-based systems. This approach benefits from advancements in smaller, more efficient models and increasingly affordable, powerful hardware. Embedded machine learning provides several advantages, including faster processing speeds without server round-trips, offline functionality, and enhanced privacy due to local data processing. However, it also faces constraints such as the need for smaller models that might reduce accuracy and challenges in implementing active learning for model improvement. In the context of computer vision, deploying models to edge devices requires setting up the necessary dependencies, often utilizing tools like Docker for managing these environments. Despite the complexity, this method offers unique benefits, such as increased inference speeds and local data processing.

In a guest post edited by the Roboflow team, data scientist Jaco Lau discusses his development of a computer vision application to detect Personal Protective Equipment (PPE) on construction sites, highlighting the critical role PPE plays in preventing injuries and fatalities. Despite the high incidence of safety infractions due to improper PPE use, Lau created a model leveraging computer vision to identify safety violations by using object detection to recognize whether PPE was worn correctly or not. Lau initially faced challenges in gathering suitable images but overcame them by manually selecting relevant data and using Roboflow to annotate and preprocess images, which included resizing them to a uniform dimension for faster training. The application prioritizes recall to ensure it captures all safety violations, albeit with some false positives. The final model was integrated into a Streamlit application for deployment, showcasing the ease of using Roboflow for image processing and Streamlit for application deployment, significantly streamlining the development process.

Paint.wtf is an online game created by Roboflow and Booste, blending AI technology with user creativity, where players submit digital drawings based on quirky prompts like "Draw a giraffe in the arctic." Leveraging OpenAI's CLIP model, the game evaluates submissions by comparing the user-generated image with the AI's interpretation of the text prompt, using cosine similarity to assign scores. Initially, a set of prompts was manually created, after which GPT-2, a generative text model, was used to expand the list, ensuring a mix of creativity and humor. The game became an internet sensation, amassing over 150,000 submissions and attracting attention on platforms like Hackers News and Reddit. To address challenges such as potential misuse through typography attacks, a text penalty was implemented, and content moderation was enforced using CLIP's ability to identify inappropriate content. Paint.wtf serves as both a playful interaction with AI and a demonstration of CLIP's capabilities and limitations in interpreting digital illustrations.

Kaylee Williams shares her insights from attending the 2021 Startup Summit virtually, emphasizing the advantages of increased accessibility through online conferences. She highlights key takeaways from talks by industry leaders such as Aviad Pinkovezky, Biz Stone, and Melissa Alvarado, focusing on the importance of designing scalable operations, being open-minded about market feedback, and building a strong team culture to drive startup success. Pinkovezky stresses the need to minimize "customer debt" through thoughtful design, while Alvarado advocates for a customer-centric approach to maintain product-market fit. Stone underscores the significance of the management team over the initial idea, advocating for a dynamic and trusting team environment. These insights reaffirm the value of virtual conferences in providing vital industry knowledge and fostering active listening to propel startups forward.

In March 2021, Roboflow focused on reinforcing its foundational infrastructure, fixing bugs, and preparing for future developments following a period of rapid feature expansion. Key updates included enhancements in Roboflow Organize, such as the introduction of Upload Batches and an Unannotated Queue to streamline labeling workflows, along with improved team collaboration features and caching mechanisms. Roboflow Train saw the groundwork laid for on-device deployments and the soft launch of roboflow.js for browser-based inference. Roboflow Infer added new preprocessing options and enhanced prediction visualization, while Roboflow Annotate boosted speed and reliability through WebGL context reuse, reduced CPU usage, and introduced a Label Assist onboarding flow. The company also announced key partnerships with OpenCV and collaborations with Paperspace and Streamlit, introduced new industry pages, and released additional content such as blog posts and YouTube videos. Highlights included welcoming a new Head of Customer Development and showcasing user projects like a cattle counter and social distancing drone.

The 2021 OpenCV AI Competition, in partnership with Roboflow, attracted over 1,200 teams worldwide to compete for more than $400,000 in prizes, focusing on innovative applications of computer vision. Key emerging trends identified in the competition include using computer vision to assist the visually impaired by providing real-time spatial awareness, enhancing workplace safety by identifying hazardous areas, and addressing COVID-19 safety through social distancing and PPE detection. Additionally, proposals explored the use of computer vision in agriculture to enhance efficiency and in animal welfare for monitoring and conservation efforts. The first round involved submitting project proposals, with over 200 teams advancing to the next phase to develop their ideas further with the support of Luxonis OAK-D devices and additional infrastructure. As the competition progresses, more details about innovative proposals and advancing teams will be shared, highlighting the potential of computer vision in various fields.

Brad Dwyer's article details the process of running Tensorflow.js on NVIDIA Jetson devices, which are AI-capable low-power computers ideal for machine learning tasks at the edge. Tensorflow.js, a library for deploying machine learning in JavaScript, can be executed in web browsers using the WebGL backend to leverage GPU acceleration, achieving modest frame rates on Jetson models like the Xavier NX and Nano. For headless setups using Node.js, users face challenges due to the lack of prebuilt binaries for the arm64 platform, necessitating manual compilation of libtensorflow and TFjs C++ bindings. Dwyer provides a detailed walkthrough of the manual build process, including configuring the necessary CUDA and CuDNN versions, highlighting the complexity and potential pitfalls involved. To simplify deployment, Roboflow offers a ready-made Docker container and npm package that streamlines the setup, allowing Tensorflow.js to run efficiently with GPU support on Jetson devices. The article underscores the potential performance improvements and practical solutions for deploying machine learning models on the edge using Tensorflow.js with Roboflow's tools.