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README.md
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pipeline_tag: image-classification
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tags:
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- climate
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-
---
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pipeline_tag: image-classification
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tags:
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- climate
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---
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## Model description
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This is a transformers based image classification model, implemented using the technique of transfer learning.
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The pretrained model is [Vision transformer](https://huggingface.co/google/vit-base-patch16-224) trained on Imagenet-21k.
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## Datasets
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The dataset used is downloaded from git repo [Agri-Hub/Space2Ground](https://github.com/Agri-Hub/Space2Ground/tree/main).
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I used Street-level image patches folder for this model. It is a dataset containing cropped vegetation parts of
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mapillary street-level images. Further details are on the linked git repo.
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### How to use
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You can use this model directly with help of pipeline class from transformers library of hugging face
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```python
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>>>from transformers import pipeline
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>>>classifier = pipeline("image-classification", model="iammartian0/vegetation_classification_model")
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>>>classifier(image)
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```
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## Training procedure
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### Preprocessing
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Assigining labels based on parent folder names
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### Image Transformations
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Applied RandomResizedCrop from torchvision.transforms to all the training images.
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### Finetuning
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Model is finetuned on the dataset for four epochs
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## Evaluation results
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### BibTeX entry and citation info
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```bibtex
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@article{DBLP:journals/corr/abs-1810-04805,
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author = {Jacob Devlin and
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Ming{-}Wei Chang and
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Kenton Lee and
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Kristina Toutanova},
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title = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language
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Understanding},
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journal = {CoRR},
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volume = {abs/1810.04805},
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year = {2018},
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url = {http://arxiv.org/abs/1810.04805},
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archivePrefix = {arXiv},
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eprint = {1810.04805},
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timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},
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biburl = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib},
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bibsource = {dblp computer science bibliography, https://dblp.org}
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}
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```
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<a href="https://huggingface.co/exbert/?model=bert-base-cased">
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<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
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</a>
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