tech4humans/Qwen2.5-3B-Instruct-lora-r16-alpha16-dp0.2-rslora
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samuellimabraz
updated
a
model
14 days ago
samuellimabraz
published
a
model
14 days ago
samuellimabraz
published
a
model
15 days ago
samuellimabraz
posted
an
update
21 days ago
Post
1667
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.
🤖 Asimo Foundation:
Using MediaPipe for gesture control of a robotic arm in an educational project.
☕ 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!
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.
🤖 Asimo Foundation:
Using MediaPipe for gesture control of a robotic arm in an educational project.
☕ 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!
samuellimabraz
posted
an
update
about 2 months ago
Post
844
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
- 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
- 1 reply
samuellimabraz
posted
an
update
3 months ago
Post
473
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), IA³.
- 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 🤗
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), IA³.
- 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 🤗
samuellimabraz
updated
a
Space
3 months ago
samuellimabraz
updated
a
model
3 months ago
samuellimabraz
updated
a
dataset
3 months ago
samuellimabraz
updated
a
collection
4 months ago
samuellimabraz
published
a
Space
4 months ago
samuellimabraz
updated
a
model
4 months ago
samuellimabraz
published
a
model
4 months ago
samuellimabraz
updated
a
Space
4 months ago