---
license: mit
tags:
- pytorch
- image-classification
- gzsl
- agriculture
- weeds
- crops
- mobilenetv2
- resnet18
- squeezenet
- shufflenetv2
- squeeze-and-excitation
- depthwise-separable-convolution
- weed-identification
---

# GZSL Weeds Identification: Lightweight Classifier Weights

This repository hosts the PyTorch checkpoints used in our generalized zero‑shot learning (GZSL) pipeline for weed identification in agricultural imagery.  
Backbones were fine‑tuned on **CropAndWeed** and evaluated for cross‑dataset generalization to **Plant Phenotyping** and a self‑collected, real‑field dataset.

## Available models

| File name | Architecture / variant |
|-----------|------------------------|
| `mobilenet.pt` | MobileNetV2 (ImageNet stem, width 1.0) |
| `resnet18.pt` | ResNet‑18 |
| `squeezenet.pt` | SqueezeNet 1.1 |
| `shufflenet.pt` | ShuffleNet V2 (baseline) |
| `shufflenet_squeeze_excitation.pt` | ShuffleNet V2 + Squeeze‑and‑Excitation (SE) |
| `shufflenet_sep_conv.pt` | ShuffleNet V2 + Depthwise Separable Convolution (SC) |
| `shufflenet_sep_conv_squeeze_excitation.pt` | ShuffleNet V2 + SC + SE |

## Getting started

All inference scripts, data loaders and architecture definitions live in the companion GitHub repository:
https://github.com/SyArsRa/WeedZSL.git


The quick‑start guide there walks through:

1. Instantiating the desired backbone (for example, MobileNetV2 or ShuffleNetV2 + SE)  
2. Loading the matching `.pt` file from this weights hub  
3. Running single‑image or batch inference  
4. Fine‑tuning on a custom dataset if needed  

## License

Weights and code are released under the MIT license for research and non‑commercial use.  
See `LICENSE` for details or contact the maintainers for alternative licensing.

## Citation

These checkpoints support a study currently submitted to a conference.  
Please cite the forthcoming paper or contact the authors for an interim reference.