
## 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}
}
```


{"language": ["en"], "license": "apache-2.0", "tags": ["climate"], "metrics": ["accuracy"], "pipeline_tag": "image-classification"}
transformers pytorch tensorboard vit image-classification climate en arxiv:2006.03677 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #climate #en #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 


## Model description

This is a transformers based image classification model, implemented using the technique of transfer learning.
The pretrained model is Vision transformer trained on Imagenet-21k.

## Datasets

The dataset used is downloaded from git repo Agri-Hub/Space2Ground.
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


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





## Model description

This is a transformers based image classification model, implemented using the technique of transfer learning.
The pretrained model is Vision transformer trained on Imagenet-21k. ## Datasets

The dataset used is downloaded from git repo Agri-Hub/Space2Ground.
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


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
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #climate #en #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ## Model description

This is a transformers based image classification model, implemented using the technique of transfer learning.
The pretrained model is Vision transformer trained on Imagenet-21k. ## Datasets

The dataset used is downloaded from git repo Agri-Hub/Space2Ground.
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


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
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #climate #en #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
## Model description

This is a transformers based image classification model, implemented using the technique of transfer learning.
The pretrained model is Vision transformer trained on Imagenet-21k.## Datasets

The dataset used is downloaded from git repo Agri-Hub/Space2Ground.
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


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