Neural networks training pipeline based on PyTorch
My repo for training neural nets using pytorch-lightning and hydra
YOLOv12 Underwater Object Detection is an open-source suite for underwater object detection, built on YOLOv12. It offers an end-to-end pipeline with GPU-accelerated training, customizable data augmentations, real-time inference via Gradio, and support for model export (ONNX & PyTorch).
Train custom wake word models with openWakeWord. A granular 13-step pipeline with compatibility patches for torchaudio 2.10+, Piper TTS, and speechbrain. Generates tiny ONNX models (~200 KB) for real-time keyword detection — like building your own "Hey Siri" trigger. WSL2/Linux + CUDA required.
End-to-end speech model training pipeline built on Burn — MFCC features, CTC loss, LibriSpeech loader, SpeechOcean762 evaluation
tracebloc notebook to launch and manage experiments in collaboration
YOLO training toolkit with Claude Code skills — dataset management, experiment tracking, HP tuning via model.tune(), active learning with CVAT, ONNX export. Supports YOLO11 & YOLO26.
Deep Learning training and deployment pipeline, reduce repetitive work from research to deployment
This repository features an image sharpening pipeline using Knowledge Distillation. A high-capacity Restormer acts as the teacher model, while a lightweight Mini-UNet is trained as the student to mimic its performance.
Immutable checkpoint storage for ML training pipelines. Kernel-level protection, anomaly detection, score-gated rollback, and self-healing recovery. Built in Rust.
Desktop GUI app for automating deep learning training on Vast.ai cloud GPUs — classification & regression with one click
Training an image classification model with CIFAR-10 dataset
Reproducible AI, ML, and scientific computing template with Pixi, PyTorch Lightning, Aim tracking, and CPU/GPU training.
🔧 Fine-tune large language models locally on your data, export to GGUF, and train on CPU with ease using the Mobius LLM Fine-Tuning Engine.
🧠 Deep-Learning Evolution: Unified collection of TensorFlow & PyTorch projects, featuring custom CUDA kernels, distributed training, memory‑efficient methods, and production‑ready pipelines. Showcases advanced GPU optimizations, from foundational models to cutting‑edge architectures. 🚀
Internship projects completed as part of the Shristi24 program offered by IIIT Hyderabad
TraceOS standardizes AI experiments into reproducible, searchable, and comparable assets. One command runs experiments, generates reports, and produces structured analysis: capability vectors, failure taxonomy, and recommendations. Every run is tracked, traceable, and comparable. Built on ABC-130K (amazon-far/abc). Apache 2.0.
Machine Learning in Production
AI Message Labels: Packaging and pipelines for deep learning text classification models
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