Samuel Lima Braz PRO

samuellimabraz

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posted an update about 14 hours ago
I recently had the opportunity to present at a Computer Vision Hangout, sharing my journey from autonomous drone competition to fine-tuning Vision-Language Models. I built an interactive presentation app! Here's a glimpse of the topics: 🚁 Black Bee Drones: My first steps into CV with Latin America's first autonomous drone team. Covering classical CV techniques (filtering, edge detection), the IMAV 2023 mission (ArUco detection, line following with PID control), and links to demos for OpenCV basics and PID simulation. πŸ€– Asimo Foundation: Using MediaPipe for gesture control of a robotic arm in an educational project. β˜• CafeDL: Building a small Deep Learning framework from scratch in Java (inspired by Keras, using ND4J) and training a CNN for a QuickDraw-like app. 🏒 Tech4Humans: Real-world applications, including open-source signature detection and efficient fine-tuning of VLMs for document extraction. Check out the interactive demos (also embedded in the main app): 1️⃣ CV Hangout App: The main presentation app showcasing my journey. https://huggingface.co/spaces/samuellimabraz/cv-hangout 2️⃣ OpenCV GUI: Real-time demo of CV techniques (filters, color filtering, ArUco) & AI models. https://huggingface.co/spaces/samuellimabraz/opencv-gui 3️⃣ Line Follow PID: Simulation of a PID controller for drone line-following. https://huggingface.co/spaces/samuellimabraz/line-follow-pid I hope these resources are helpful to someone on their CV learning journey!
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samuellimabraz's activity

posted an update about 14 hours ago
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I recently had the opportunity to present at a Computer Vision Hangout, sharing my journey from autonomous drone competition to fine-tuning Vision-Language Models.

I built an interactive presentation app! Here's a glimpse of the topics:

🚁 Black Bee Drones:
My first steps into CV with Latin America's first autonomous drone team. Covering classical CV techniques (filtering, edge detection), the IMAV 2023 mission (ArUco detection, line following with PID control), and links to demos for OpenCV basics and PID simulation.

πŸ€– Asimo Foundation:
Using MediaPipe for gesture control of a robotic arm in an educational project.

β˜• CafeDL:
Building a small Deep Learning framework from scratch in Java (inspired by Keras, using ND4J) and training a CNN for a QuickDraw-like app.

🏒 Tech4Humans:
Real-world applications, including open-source signature detection and efficient fine-tuning of VLMs for document extraction.

Check out the interactive demos (also embedded in the main app):

1️⃣ CV Hangout App: The main presentation app showcasing my journey.
samuellimabraz/cv-hangout

2️⃣ OpenCV GUI: Real-time demo of CV techniques (filters, color filtering, ArUco) & AI models.
samuellimabraz/opencv-gui

3️⃣ Line Follow PID: Simulation of a PID controller for drone line-following.
samuellimabraz/line-follow-pid

I hope these resources are helpful to someone on their CV learning journey!
posted an update about 1 month ago
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Sharing my new article on an open-source project for automated signature detection in document processing. The article details:

- Dataset Engineering: Combining two public collections to create a hybrid dataset.
- Architecture Benchmarking: Evaluating sota models such as YOLO series, DETR variants, and YOLOS for accuracy and efficiency.
- Model Optimization: Using Optuna for hyperparameter tuning, achieving a 7.94% F1-score improvement.
- Production Deployment: Implementing Triton Inference Server with an OpenVINO CPU backend for optimized inference.

It's not such a complex project, but I explore the training of the best current architectures for object detection and share all notebooks, data, models, and the repo with deployment and benchmarking details.

Thanks @SkalskiP , @nielsr , @sergiopaniego for the notebooks and resources that have been very helpful.

- https://huggingface.co/blog/samuellimabraz/signature-detection-model
- tech4humans/signature-detection-678b087d8b0ce22ae8c3f60e
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posted an update 3 months ago
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I wrote a article on Parameter-Efficient Fine-Tuning (PEFT), exploring techniques for efficient fine-tuning in LLMs, their implementations, and variations.

The study is based on the article "Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning" and the PEFT library integrated with Hugging Face's Transformers.

Article: https://huggingface.co/blog/samuellimabraz/peft-methods
Notebook: https://colab.research.google.com/drive/1B9RsKLMa8SwTxLsxRT8g9OedK10zfBEP?usp=sharing
Collection: samuellimabraz/service-summary-6793ccfe774073328ea9f8df

Analyzed methods:
- Adapters: Soft Prompts (Prompt Tuning, Prefix Tuning, P-tuning), IAΒ³.
- Reparameterization: LoRA, QLoRA, LoHa, LoKr, X-LoRA, Intrinsic SAID, and variations of initializations (PiSSA, OLoRA, rsLoRA, DoRA).
- Selective Tuning: BitFit, DiffPruning, FAR, FishMask.

I'm starting out in generative AI, I have more experience with computer vision and robotics. Just sharing here πŸ€—