https://github.com/mr-talhailyas/cwd30
Official Repository for CWD30 Dataset
https://github.com/mr-talhailyas/cwd30
crops-and-weeds crops-dataset dataset plant-classification precision-agriculture weeds-data
Last synced: 30 days ago
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Official Repository for CWD30 Dataset
- Host: GitHub
- URL: https://github.com/mr-talhailyas/cwd30
- Owner: Mr-TalhaIlyas
- Created: 2023-05-11T06:09:04.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2025-01-19T02:25:03.000Z (10 months ago)
- Last Synced: 2025-01-19T03:20:10.273Z (10 months ago)
- Topics: crops-and-weeds, crops-dataset, dataset, plant-classification, precision-agriculture, weeds-data
- Language: Python
- Homepage: https://arxiv.org/abs/2305.10084
- Size: 3.63 MB
- Stars: 34
- Watchers: 2
- Forks: 6
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# CWD30 | [Project Page](https://cwd-30.github.io/cwd-30/)
# Data Download [Open Link](https://cwd-30.github.io/cwd-30/download.html)
#### Full Paper [COMPAG](https://www.sciencedirect.com/science/article/pii/S0168169924011281)
CWD30 comprises over 219,770 high-resolution images of 20 weed species and 10 crop species, encompassing various growth stages, multiple viewing angles, and environmental conditions. The images were collected from diverse agricultural fields across different geographic locations and seasons, ensuring a representative dataset.
##### [*If you use our data/paper in your projects kindly **cite** the paper and **star** the repo*]().
### Global Distribution of Crops in the CWD30 dataset.
## MODEL ZOO
Classification Models
⚠️NOTE⚠️ We are currently in middle of uploading the weights. All might not be available.
|Model|Weights|Acc|
|---|---|---|
|ResNet-18|[chkpt]()|79.5|
|ResNet-50|[chkpt]()|84.6|
|ResNet-101|[chkpt]()|81.36|
|MobileNetv3-S|[chkpt]()|80.5|
|MobileNetv3-L|[chkpt]()|74.67|
|EffNet-B0|[chkpt]()|83.2|
|EffNet-B3|[chkpt]()|83.64|
|EffNet-B5|[chkpt]()|84.5|
|ConvNeXt-T|[chkpt]()|85.6|
|ConvNeXt-M|[chkpt]()|85.9|
|ConvNeXt-L|[chkpt]()|84.7|
|ViT-T|[chkpt]()|83.43|
|ViT-B|[chkpt]()|86.4|
|CaiT-T|[chkpt]()|85.2|
|CaiT-S|[chkpt]()|86.9|
|Swin-T|[chkpt]()|85.59|
|Swin-B|[chkpt]()|85.3|
|Swin-L|[chkpt]()|87.0|
|MaxViT-S|[chkpt]()|86.5|
|MaxViT-B|[chkpt]()|87.08|
|CoAtNet-1|[chkpt]()|86.1|
|CoAtNet-3|[chkpt]()|84.3|
|EffFormer-L1|[chkpt]()|80.5|
|EffFormer-L3|[chkpt]()|82.7|
|EffFormer-L7|[chkpt]()|81.2|
### Pretrained Weights on iNaturalist
|ReNet-101|Weights|Acc.|
|---|---|---|
|[iNat21](https://github.com/visipedia/inat_comp/tree/master/2021)|✅[chkpt](https://o365jbnu-my.sharepoint.com/:u:/g/personal/talha_student_jbnu_ac_kr/Eej_bdo_W4VMjG6GHfr3YS8B3sIJKeN32xXGI5rr4O_ajg?e=0N96wn)|<80%|
|[iNat17](https://github.com/visipedia/inat_comp/tree/master/2017)|✅[chkpt](https://o365jbnu-my.sharepoint.com/:u:/g/personal/talha_student_jbnu_ac_kr/EeeOI8gv3mxAg7dx6HXVanQBp_5dq4BFDpyyJ5CxQ-KpGQ?e=X4uNub)|60.41%|
Semantic Segmentation Models
Access dataset via:
* [Sugar Beet](https://www.ipb.uni-bonn.de/data/sugarbeets2016/)
* [Carrot Weed](https://github.com/cwfid/dataset)
* [Bean Weed](https://o365jbnu-my.sharepoint.com/personal/talha_student_jbnu_ac_kr/_layouts/15/onedrive.aspx?ga=1&id=%2Fpersonal%2Ftalha%5Fstudent%5Fjbnu%5Fac%5Fkr%2FDocuments%2FDatasets%2FBean%20UDA)
⚠️NOTE⚠️ We are currently in middle of uploading the weights. All might not be available.
|Model |BeanWeed |SugarBeet |CarrotWeed |
|--- |--- |--- |--- |
|UNet |✅[72.49 mIOU, chkpt](https://o365jbnu-my.sharepoint.com/:u:/g/personal/talha_student_jbnu_ac_kr/EaHpmYLSs6dJmfVMlp4wjMwBqnnAJQz4QoskdSeKyN_mWw?e=iQ1dCA) |✅[85.47 mIOU, chkpt](https://o365jbnu-my.sharepoint.com/:u:/g/personal/talha_student_jbnu_ac_kr/EZv7lWyh8sJJngFz3mhEfegBpxhEgBENA1UYYOmLw7OboA?e=02UuxA) |✅[78.32 mIOU, chkpt](https://o365jbnu-my.sharepoint.com/:u:/g/personal/talha_student_jbnu_ac_kr/ESV2mP0mfqBEqXf4U0JYnVQBPgWujDMlU4ybhSdDtrHW9g?e=eDhpcW) |
|DeepLab v3+ |[78.03 mIOU, chkpt]() |[86.02 mIOU, chkpt]() |✅[83.16 mIOU, chkpt](https://o365jbnu-my.sharepoint.com/:u:/g/personal/talha_student_jbnu_ac_kr/EVkfmjyMmapNii0jRxncs5UB4Ipi3qYiMNPEF4lQc6g_-w?e=9LtjUe) |
|OCR |[79.51 mIOU, chkpt]() |[87.34 mIOU, chkpt]() |✅[86.53 mIOU, chkpt](https://o365jbnu-my.sharepoint.com/:u:/g/personal/talha_student_jbnu_ac_kr/EQvrRiTSwBlBniZGkYicFl8Bf4pUPhLyJeBWCUD6LOCW6Q?e=hWyD5E) |
|SegNext |[83.90 mIOU, chkpt]() |[87.65 mIOU, chkpt]() |✅[88.54 mIOU, chkpt](https://o365jbnu-my.sharepoint.com/:u:/g/personal/talha_student_jbnu_ac_kr/EdI8iQLqhX9LvgTKAFGyHMEBhqc4wcBW7yOVNKb9q78j3A?e=AvJhty) |
✅[MSCAN backbone SegNext](https://o365jbnu-my.sharepoint.com/:u:/g/personal/talha_student_jbnu_ac_kr/EQdev4A2alhOjhFLw5hxoOwBkIfW6tTD_RD9ElF1AqpvEA?e=s8D7oi)
Instances Segmentation Models
Access dataset via:
* [PhenoBench](https://www.phenobench.org/)
* [GrowliFlower](https://rs.ipb.uni-bonn.de/data/growliflower/)
|Model|Data|Weights|PQ|
|---|---|---|---|
|MaskRCNN (ResNet-101 FPN backbone)|PhenoBench|✅[chkpt](https://o365jbnu-my.sharepoint.com/:u:/g/personal/talha_student_jbnu_ac_kr/EZsslt1DqAlGnNLFPX67AAABcyLAQOayNRM_K_Me-yyCeA?e=J2eWBy)|44.05|
|MaskRCNN (ResNet-101 FPN backbone)|GrowliFlower|✅[chkpt](https://o365jbnu-my.sharepoint.com/:u:/g/personal/talha_student_jbnu_ac_kr/EUVXn6Az9fxEjgsCHJA4BMUB5O-S0x0U_C22NP__-AT6aQ?e=uFPxbr)|56.33|
### Citation
```
@article{ilyas2025cwd30,
title={CWD30: A new benchmark dataset for crop weed recognition in precision agriculture},
author={Ilyas, Talha and Arsa, Dewa Made Sri and Ahmad, Khubaib and Lee, Jonghoon and Won, Okjae and Lee, Hyeonsu and Kim, Hyongsuk and Park, Dong Sun},
journal={Computers and Electronics in Agriculture},
volume={229},
pages={109737},
year={2025},
publisher={Elsevier}
}
```