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Marco Franzon

@mfranz_on • 14,349 subscribers

Computer vision · ESP32 projects · Agentic AI @ dualistic · python @ exact-lab

Shorts

I received a message in my direct messages asking how difficult it would be to use computer vision to track cell movement. > SAM zero shot for the immune cell > YOLO finetuned on a small dataset for bacteria Realization time: ~2 hours

I received a message in my direct messages asking how difficult it would be to use computer vision to track cell movement. > SAM zero shot for the immune cell > YOLO finetuned on a small dataset for bacteria Realization time: ~2 hours

185,993 просмотров

This is the power of YOLO, trained on a laptop for ~1 hour, with a Kaggle dataset. Oh, and just ~100 lines of Python. I can make a startup on this and it took me literally a couple of hours.

This is the power of YOLO, trained on a laptop for ~1 hour, with a Kaggle dataset. Oh, and just ~100 lines of Python. I can make a startup on this and it took me literally a couple of hours.

4,858,878 просмотров

The future of agriculture lies in optimization. A system like this costs very little: a drone with a camera and a computer vision system accurate enough to count and identify plants or diseases. It's so easy that anyone can make it at home for their own crops.

The future of agriculture lies in optimization. A system like this costs very little: a drone with a camera and a computer vision system accurate enough to count and identify plants or diseases. It's so easy that anyone can make it at home for their own crops.

118,582 просмотров

Now you can make the so famous "pothole detector" using Ultralytics Platform as backend. All the compute is offloaded, you have just to make a cool UI to have your detector.

Now you can make the so famous "pothole detector" using Ultralytics Platform as backend. All the compute is offloaded, you have just to make a cool UI to have your detector.

158,374 просмотров

The "hello world" was achieved. Soon everyone can build cool ESP32 projects starting from a prompt. And look the beautiful simulation.

The "hello world" was achieved. Soon everyone can build cool ESP32 projects starting from a prompt. And look the beautiful simulation.

65,791 просмотров

I can't stop playing with Rapid Roboflow. The process is so smooth that you can just: - Go to the rapid roboflow platform - Drop a few images or a short video - Type a prompt like “yellow cap” or “person wearing red hat” - Instantly get a working detection model No training, no GPUs, no cloud setup. Plug it straight into your app. It’s also baked into Roboflow Workflows, so you can instantly build automations like “count vials crossing this line” in minutes. Computer vision just went from weeks in seconds.

I can't stop playing with Rapid Roboflow. The process is so smooth that you can just: - Go to the rapid roboflow platform - Drop a few images or a short video - Type a prompt like “yellow cap” or “person wearing red hat” - Instantly get a working detection model No training, no GPUs, no cloud setup. Plug it straight into your app. It’s also baked into Roboflow Workflows, so you can instantly build automations like “count vials crossing this line” in minutes. Computer vision just went from weeks in seconds.

175,899 просмотров

Added an obstacle in the room and simulated three different scenarios. This makes any kind of airflow simulation inside a room much more accessible. You can optimize the ventilation of your living room with just 10 lines of Python.

Added an obstacle in the room and simulated three different scenarios. This makes any kind of airflow simulation inside a room much more accessible. You can optimize the ventilation of your living room with just 10 lines of Python.

142,423 просмотров

Calculating pothole volume from a 2D image is challenging but possible: > Assign each pothole an ID to avoid duplication. > Use basic trigonometry to convert pixels to real-world meters. > Apply a novel algorithm to estimate depth using road engineering data.

Calculating pothole volume from a 2D image is challenging but possible: > Assign each pothole an ID to avoid duplication. > Use basic trigonometry to convert pixels to real-world meters. > Apply a novel algorithm to estimate depth using road engineering data.

133,860 просмотров

wait... this looks 98% accurate

wait... this looks 98% accurate

47,856 просмотров

Curling stone tracking v1 Olympic Games edition. How I made this first version: - YOLO for real-time stone detection - ByteTrack (Supervision) for consistent object tracking - SciPy for trajectory smoothing - OpenCV + NumPy for annotation, velocity, direction and acceleration visualization The colors indicate whether the stone is : accelerating → green stable → orange decelerating → red

Curling stone tracking v1 Olympic Games edition. How I made this first version: - YOLO for real-time stone detection - ByteTrack (Supervision) for consistent object tracking - SciPy for trajectory smoothing - OpenCV + NumPy for annotation, velocity, direction and acceleration visualization The colors indicate whether the stone is : accelerating → green stable → orange decelerating → red

20,522 просмотров

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Camouflage no longer works with YOLO. The detection of the hays here is impressive. YOLO can identify an hay which has mostly the same color of the background.

Camouflage no longer works with YOLO. The detection of the hays here is impressive. YOLO can identify an hay which has mostly the same color of the background.

149,156 просмотров • 1 месяц назад

I created a web app for teaching computer vision basics, allowing users to upload an image or video stream and instantly view it as a numerical pixel matrix, with options to display values in RGB, Hex, or Grayscale formats. It features predefined convolution kernels applied in real-time: - Sobel operator for edge detection (computes gradients to highlight boundaries) - Gaussian Blur: averages neighboring pixels with weights to reduce noise - Sharpen: boosts local contrast to enhance details This setup clearly visualizes how images are represented as matrices or tensors, how convolution slides kernels across pixels, and how operations like gradient computation, blurring, or sharpening modify the data step-by-step. I plan to use demos like this in my upcoming computer vision course to build intuition for fundamental image processing concepts, OpenCV filters, and CNN building blocks (e.g., why kernels extract features).

I created a web app for teaching computer vision basics, allowing users to upload an image or video stream and instantly view it as a numerical pixel matrix, with options to display values in RGB, Hex, or Grayscale formats. It features predefined convolution kernels applied in real-time: - Sobel operator for edge detection (computes gradients to highlight boundaries) - Gaussian Blur: averages neighboring pixels with weights to reduce noise - Sharpen: boosts local contrast to enhance details This setup clearly visualizes how images are represented as matrices or tensors, how convolution slides kernels across pixels, and how operations like gradient computation, blurring, or sharpening modify the data step-by-step. I plan to use demos like this in my upcoming computer vision course to build intuition for fundamental image processing concepts, OpenCV filters, and CNN building blocks (e.g., why kernels extract features).

270,251 просмотров • 5 месяцев назад

Roboflow Rapid has revolutionized computer vision. You can train, deploy, and use a custom model with just one prompt and a video. I replicated my pothole detector in one minute on their platform by uploading an unlabeled video and writing "potholes".

Roboflow Rapid has revolutionized computer vision. You can train, deploy, and use a custom model with just one prompt and a video. I replicated my pothole detector in one minute on their platform by uploading an unlabeled video and writing "potholes".

195,470 просмотров • 5 месяцев назад

This is MicroCAD. The programming language for your 3D projects. You can design your own Lego block in a few lines of code, ready to be exported as an STL file for 3D printing.

This is MicroCAD. The programming language for your 3D projects. You can design your own Lego block in a few lines of code, ready to be exported as an STL file for 3D printing.

113,663 просмотров • 6 месяцев назад

The Future of gaming is awesome. No extra hardware required, you just need a normal pc and a camera to have a totally immersive experience. > mediapipe is going to be the next big thing

The Future of gaming is awesome. No extra hardware required, you just need a normal pc and a camera to have a totally immersive experience. > mediapipe is going to be the next big thing

103,236 просмотров • 6 месяцев назад

You can build your 3D models directly from a prompt into Claude code. All thanks to this beautiful python project, build123 a parametric modeling framework for 2D and 3D CAD. It is built on the Open Cascade geometric kernel and it is suitable for 3D printing.

You can build your 3D models directly from a prompt into Claude code. All thanks to this beautiful python project, build123 a parametric modeling framework for 2D and 3D CAD. It is built on the Open Cascade geometric kernel and it is suitable for 3D printing.

20,715 просмотров • 2 месяцев назад

I need to annotate some images for training a computer vision model. There are many powerful annotation platforms available, but I want to keep my images local. I added a new section to my CV Streamlit app to quickly annotate images and train a YOLO model in a few clicks.

I need to annotate some images for training a computer vision model. There are many powerful annotation platforms available, but I want to keep my images local. I added a new section to my CV Streamlit app to quickly annotate images and train a YOLO model in a few clicks.

29,530 просмотров • 4 месяцев назад

I want to detect the gates to check how many were missed. But it is pretty complex: - Gates change size dramatically based on distance - No unique features: very generic shape, no distinctive texture or pattern - Model becomes biased toward detecting "gate-like" things

I want to detect the gates to check how many were missed. But it is pretty complex: - Gates change size dramatically based on distance - No unique features: very generic shape, no distinctive texture or pattern - Model becomes biased toward detecting "gate-like" things

12,957 просмотров • 4 месяцев назад

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