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---
license: apache-2.0
language:
- en
metrics:
- accuracy
pipeline_tag: image-classification
tags:
- climate
---
## Model description
This is a transformers based image classification model, implemented using the technique of transfer learning.
The pretrained model is [Vision transformer](https://huggingface.co/google/vit-base-patch16-224) trained on Imagenet-21k.
## Datasets
The dataset used is downloaded from git repo [Agri-Hub/Space2Ground](https://github.com/Agri-Hub/Space2Ground/tree/main).
I used Street-level image patches folder for this model. It is a dataset containing cropped vegetation parts of
mapillary street-level images. Further details are on the linked git repo.
### How to use
You can use this model directly with help of pipeline class from transformers library of hugging face
```python
>>>from transformers import pipeline
>>>classifier = pipeline("image-classification", model="iammartian0/vegetation_classification_model")
>>>classifier(image)
```
or
uploading a target image to Hosted inference api.
## Training procedure
### Preprocessing
Assigining labels based on parent folder names
### Image Transformations
Applied RandomResizedCrop from torchvision.transforms to all the training images.
### Finetuning
Model is finetuned on the dataset for four epochs
## Evaluation results
Model acheived an Top-1 accuracy of 0.929.
## Further exploration to do
- Trainig a multilabel model where model can find if the image is from left side or right side
on top of classifying the vegetation
- Fine grained classification of crop labels using Raw/Initial set of street-level images
### BibTeX entry and citation info
```bibtex
@misc{wu2020visual,
title={Visual Transformers: Token-based Image Representation and Processing for Computer Vision},
author={Bichen Wu and Chenfeng Xu and Xiaoliang Dai and Alvin Wan and Peizhao Zhang and Zhicheng Yan and Masayoshi Tomizuka and Joseph Gonzalez and Kurt Keutzer and Peter Vajda},
year={2020},
eprint={2006.03677},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
```bibtex
@INPROCEEDINGS{9816335,
author={Choumos, George and Koukos, Alkiviadis and Sitokonstantinou, Vasileios and Kontoes, Charalampos},
booktitle={2022 IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP)},
title={Towards Space-to-Ground Data Availability for Agriculture Monitoring},
year={2022},
volume={},
number={},
pages={1-5},
doi={10.1109/IVMSP54334.2022.9816335}
}
```
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