666DZY666/micronet
micronet, a model compression and deploy lib. compression: 1、quantization: quantization-aware-training(QAT), High-Bit(>2b)(DoReFa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、Low-Bit(≤2b)/Ternary and Binary(TWN/BNN/XNOR-Net); post-training-quantization(PTQ), 8-bit(tensorrt); 2、 pruning: normal、regular and group convolutional channel pruning; 3、 group convolution structure; 4、batch-normalization fuse for quantization. deploy: tensorrt, fp32/fp16/int8(ptq-calibration)、op-adapt(upsample)、dynamic_shape observed · 2026-08-28
Health v2 · maintenance only
41/100
- Activity 20
- Release rhythm 35
- Longevity 100
Flags: no_releases
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 2464
- days_rel: n/a
- days_push: 484
- n_releases_24m: 0
Adoption not part of the score
2266 stars · 470 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
micronet is a Python library for deep neural network model compression and deployment built on PyTorch. It provides quantization (QAT, PTQ, high-bit and low-bit/ternary/binary schemes like DoReFa, TWN, BNN, XNOR-Net), convolutional channel pruning, batch-normalization fusion, and TensorRT deployment with fp32/fp16/int8 support.
Use cases
- quantize a pytorch model to low-bit or binary weights for edge deployment
- compress a resnet with channel pruning before deploying to mobile
- convert a trained model to int8 with tensorrt post-training calibration
- fuse batch normalization layers into convolution weights for quantized inference
- deploy a model with tensorrt using dynamic shapes and fp16
- implement DoReFa or XNOR-Net style quantization-aware training
When to choose
- you need both quantization and pruning in one pytorch library
- you want low-bit (ternary/binary) quantization methods like TWN, BNN, or XNOR-Net
- your deployment target is NVIDIA GPUs via TensorRT with int8 calibration
- you need batch-normalization fusion tailored to quantized models
When to avoid
- you need compression for transformers or LLMs rather than convolutional networks
- you target non-TensorRT runtimes like ONNX Runtime or Core ML exclusively
- you want a maintained production framework with broad community support rather than a research-oriented codebase
Facets
library · maturity active
machine-learning deep-learning llm-training deep-learning machine-learning gpu-computing python model-compression quantization pruning quantization-aware-training post-training-quantization tensorrt onnx dorefa binary-neural-networks network-slimming bn-fuse pytorch gpu
2 sources
- readme: https://github.com/666DZY666/micronet · fetched 2026-08-28 · 8f5b5af14999
- registry_pypi: https://pypi.org/pypi/micronet/json · fetched 2026-08-29 · 0300b461257c
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| 666DZY666/micronet | main | 41 |
For agents
markdown · JSON · MCP: product_card(name="666DZY666/micronet")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem