forresti/SqueezeNet resource

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

2218 stars · 717 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

SqueezeNet is a deep convolutional neural network architecture that achieves AlexNet-level image classification accuracy with 50x fewer parameters and a model under 0.5MB. The repository provides Caffe-compatible model definition files, solver configurations, and pretrained model weights.

Use cases

• run image classification with a tiny pretrained model
• deploy a CNN on resource-constrained or embedded devices
• study a compact CNN architecture for research
• fine-tune a small pretrained ImageNet model
• reproduce SqueezeNet training results in Caffe

When to choose

• you need AlexNet-level accuracy with a very small model footprint
• you work in Caffe and want pretrained weights and training configs
• you are researching model compression or efficient architectures

When to avoid

• you need a modern framework like PyTorch or TensorFlow
• you need state-of-the-art accuracy rather than a small footprint
• you need actively maintained code - this is primarily a model release, not a maintained library

Facets

dataset · maturity maintenance

deep-learning machine-learning image-processing deep-learning computer-vision machine-learning cpp python caffe pretrained-model model-architecture image-classification model-compression research-paper gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="forresti/SqueezeNet")