Create README.md

README.md

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+---
+datasets:
+- competitions/aiornot
+language:
+- en
+metrics:
+- accuracy
+pipeline_tag: image-classification
+---
+
+# Model Card for Model Soups on AirorNot Dataset
+
+## Model Details
+
+### Model Description
+
+Code implementation of the paper [Model soups: averaging weights of multiple fine-tuned models
+improves accuracy without increasing inference time](https://arxiv.org/abs/2203.05482). 
+
+In recent years, finetuning large models has been proving to be an excellent strategy to achieve high-performances in downstream tasks.
+The conventional recipe to do so, it's to fine-tune models with different hyperparameters and select the one achieving the highest accuracy. However Wortsman *et. al* proved that averaging the weights of multiple models finetuned with different hyperparameter configurations can actually inprove accuracy and robustness. 
+
+I read this paper recently and I felt intrigued by the powerful yet simple idea (achieving a SOTA result on Imagenet of s 90.94%) so I decided that this could be an opportunity to get my hands dirty and dive into the code and...try the soup! 
+
+I started by using the official [code implementation](https://github.com/mlfoundations/model-soups) with CLIP ViT-B/32 and finetuned only 5 of their models on AiorNot. I used a simple strategy with minimal modifications. Mainly, I finetuned the models for 8 epochs with a batch size of 56 samples. 
+
+The tricky part was that I had to modify the baseline to use it with our custom dataset.
+
+To implement this notebook I modified the version by [Cade Gordon](https://cadegordon.io/).
+
+
+- **Developed by:** HuggingSara
+- **Model type:** Computer Vision
+- **Language :** Python 
+
+
+