metadata

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:

Instantiating the desired backbone (for example, MobileNetV2 or ShuffleNetV2 + SE)
Loading the matching .pt file from this weights hub
Running single‑image or batch inference
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.