
# 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.


- **Developed by:** HuggingSara
- **Model type:** Computer Vision
- **Language :** Python 




{"language": ["en"], "datasets": ["competitions/aiornot"], "metrics": ["accuracy"], "pipeline_tag": "image-classification"}
image-classification en dataset:competitions/aiornot arxiv:2203.05482 region:us
TAGS
#image-classification #en #dataset-competitions/aiornot #arxiv-2203.05482 #region-us 


# 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. 

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 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.


- Developed by: HuggingSara
- Model type: Computer Vision
- Language : Python 




# 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. 

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 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.


- Developed by: HuggingSara
- Model type: Computer Vision
- Language : Python
TAGS
#image-classification #en #dataset-competitions/aiornot #arxiv-2203.05482 #region-us 
 # 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. 

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 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.


- Developed by: HuggingSara
- Model type: Computer Vision
- Language : Python
TAGS
#image-classification #en #dataset-competitions/aiornot #arxiv-2203.05482 #region-us 
# 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. 

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 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.


- Developed by: HuggingSara
- Model type: Computer Vision
- Language : Python