introduction.md

# Italian CLIP

With a few tricks, we have been able to fine-tune a competitive Italian CLIP model with **only 1.4 million** training samples. Our Italian CLIP model
is built upon the [Italian BERT](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) model provided by [dbmdz](https://huggingface.co/dbmdz) and the OpenAI
[vision transformer](https://huggingface.co/openai/clip-vit-base-patch32).

In building this project we kept in mind the following principles:

+ **Novel Contributions**: We created a dataset of ~1.4 million Italian image-text pairs and, to the best of our knowledge, we trained the best Italian CLIP model currently in existence;
+ **Scientific Validity**: Claim are easy, facts are hard. That's why validation is important to assess the real impact of a model. We thoroughly evaluated our models in several tasks and made the validation reproducible for everybody.
+ **Broader Outlook**: We always kept in mind which are the possible usages for this model.

We put our **hearts** and **souls** into the project during this week! Not only did we work on a cool project, but we were
able to make new friends and and learn a lot from each other to work towards a common goal! 
Thank you for this amazing opportunity, we hope you will like the results. :heart:

# Demo

In this demo, we present two tasks:

+ *Text to Image*: This task is essentially an image retrieval task. The user is asked to input a string of text and CLIP is going to
compute the similarity between this string of text with respect to a set of images. The webapp is going to display the images that
have the highest similarity with the text query.

+ *Image to Text*: This task is essentially a zero-shot image classification task. The user is asked for an image and for a set of captions/labels and CLIP
is going to compute the similarity between the image and each label. The webapp is going to display a probability distribution over the captions.

# Novel Contributions

The original CLIP model was trained on 400 million image-text pairs; this amount of data is not available for Italian. 
We indeed worked in a **low-resource setting**. The only datasets for Italian captioning in the literature are MSCOCO-IT (a translated version of MSCOCO) and WIT. 
To get competitive results we followed three strategies:
  1. more and better data;
  2. better augmentations;
  3. better training.

## More and Better Data

We eventually had to deal with the fact that we do not have the same data that OpenAI had during the training of CLIP.
Thus, we tried to add as much data as possible while keeping the data-quality as high as possible.

We considered three main sources of data:

+ [WIT](https://github.com/google-research-datasets/wit) is an image-caption dataset collected from Wikipedia (see, 
[Srinivasan et al., 2021](https://arxiv.org/pdf/2103.01913.pdf)). We focused on the *Reference Description* captions described in the paper as they are
the ones of highest quality. Nonetheless, many of these captions describe ontological knowledge and encyclopedic facts (e.g., Roberto Baggio in 1994). 
However, this kind of text, without more information, is not useful to learn a good mapping between images and captions. 
  On the other hand, this text is written in Italian and it is of good quality. We cannot just remove short captions as some of those
  are still good (e.g., "running dog"). Thus, to prevent polluting the data with captions that are not meaningful, we used *POS tagging* 
  on the text and removed all the captions that were composed for the 80% or more by PROPN (around ~10% of the data). This is a simple solution that allowed us to retain much
  of the dataset, without introducing noise. 
  
  Captions like: *'Dora Riparia', 'Anna Maria Mozzoni', 'Joey Ramone Place', 'Kim Rhodes', 'Ralph George Hawtrey' * have been removed.

+ [MSCOCO-IT](https://github.com/crux82/mscoco-it). This image-caption dataset comes from the work by [Scaiella et al., 2019](http://www.ai-lc.it/IJCoL/v5n2/IJCOL_5_2_3___scaiella_et_al.pdf). The captions comes from the original 
MSCOCO dataset and have been translated with Microsoft Translator. The 2017 version of the MSCOCO training set contains more than
100K images, for each image more than one caption is available.

+ [Conceptual Captions](https://ai.google.com/research/ConceptualCaptions/). This image-caption dataset comes from 
the work by [Sharma et al., 2018](https://aclanthology.org/P18-1238.pdf). There are more than 3mln image-caption pairs in
this dataset and these have been collected from the web. We downloaded the images with the URLs provided by the dataset, but we
could not retrieve them all. Eventually, we had to translate the captions to Italian. We have been able to collect
a dataset with 700K translated captions.


## Better Augmentations

We knew that without a good augmentation strategy we could never get competitive results to a model trained on 400 million images. Therefor we implemented heavy augmentations to make the training more data efficient. We made sure to keep hue augmentations limited however to still give the model the ability to learn color definitions. While we would have liked to have augmentations for the captions as well after some experimentation we settled with random sampling from the five captions available in MSCOCO and leaving the rest of the captions unmodified. 

## Better Training

After different trials, we realized that the usual way of training this model was
not good enough to get good results. We thus modified two different parts of the
training pipeline: the optimizer and the training with frozen components.

### Optimizer

While the initial code used AdamW as an optimizer we soon noticed that it introduced some bad properties into the training. The model strated to overfit relatively quickly and the weight decay made this effect worse. We eventually decided to an optimization strategy that had worked well for us in similar cases and used AdaBelief with Adaptive Gradient Clipping (AGC) and a Cosine Annealing Schedule. Together with slightly tuning the learning rate this helped us to reduce the validation loss by 25%.
Our implementation is available online [here](https://github.com/clip-italian/clip-italian/blob/master/hybrid_clip/run_hybrid_clip.py#L667).

### Backbone Freezing

The ViT used by OpenAI was already trained on 400 million images and it is the element in our architecture that probably required less training.
The same is true for the BERT model we use. To allow the randomly initialized Re-projection Layers to warm up without messing with the tuned weights of the backbones we decided to do a first training with the backbones of our architecture completely frozen. Only after these layers converged we unfreezed the rest of the model to fine-tune all the components. This technique allowed us to reach a much better validation loss.

<img src="https://huggingface.co/spaces/clip-italian/clip-italian-demo/raw/main/static/img/clip-italian.png" alt="drawing" width="80%"/>

# Scientific Validity

## Quantitative Evaluation
Those images are definitely cool and interesting, but a model is nothing without validation.
To better understand how well our clip-italian model works we run an experimental evaluation. Since this is the first clip-based model in Italian, we used the multilingual CLIP model as a comparison baseline. 

### mCLIP

The multilingual CLIP (henceforth, mCLIP), is a model introduced by [Nils Reimers](https://www.sbert.net/docs/pretrained_models.html) in his
[sentence-transformer](https://www.sbert.net/index.html) library. mCLIP is based on a multilingual encoder
that was created through multilingual knowledge distillation (see [Reimers et al., 2020](https://aclanthology.org/2020.emnlp-main.365/)).

### Experiments Replication
We provide two colab notebooks to replicate both experiments.

### Tasks

We selected two different tasks: 
+ image-retrieval 
+ zero-shot classification

### Image Retrieval

This experiment is run against the MSCOCO-IT validation set (that we haven't used in training). Given in input
a caption, we search for the most similar image in the MSCOCO-IT validation set. As evaluation metrics
we use the MRR.

| MRR             | CLIP-Italian | mCLIP |
| --------------- | ------------ |-------|
| MRR@1           | **0.3797**   | 0.2874|   
| MRR@5           | **0.5039**   | 0.3957|
| MRR@10          | **0.5204**   | 0.4129|

It is true that we used MSCOCO-IT in training, and this might give us an advantage. However the original CLIP model was trained
on 400million images (and some of them probably were from MSCOCO).

[Colab: Image Retrieval Evaluation](https://colab.research.google.com/drive/1bLVwVKpAndpEDHqjzxVPr_9nGrSbuOQd?usp=sharing)

### Zero-shot image classification

This experiment replicates the original one run by OpenAI on zero-shot image classification on ImageNet. To do this, we used DeepL to
translate the image labels in ImageNet with DeepL. We evaluate the models computing the accuracy. 


| Accuracy        | CLIP-Italian | mCLIP |
| --------------- | ------------ |-------|
| Accuracy@1      |  **22.11**   | 20.15 |   
| Accuracy@5      |  **43.69**   | 36.57 |
| Accuracy@10     |  **52.55**   | 42.91 |
| Accuracy@100    |  **81.08**   | 67.11 |

[Colab: ImageNet Zero Shot Evaluation](https://colab.research.google.com/drive/1zfWeVWY79XXH63Ci-pk8xxx3Vu_RRgW-?usp=sharing)

Our results confirm that CLIP-Italian is very competitive and beats mCLIP on the two different task
we have been testing. Note, however, that our results are lower than those shown in the original OpenAI
paper (see, [Radford et al., 2021](https://arxiv.org/abs/2103.00020)). However, considering that our results are in line with those obtained by mCLIP we think that 
the translated image labels might have had an impact on the final scores.

## Qualitative Evaluation

We hereby show some very interesting properties of the model. One is its ability to detect colors, 
then there is its (partial) counting ability and finally the ability of understanding more complex quries. To our own surprise, many of the answers the model gives make a lot of sense! 
Look at the following - slightly cherry picked (but not even that much) - examples:

### Colors
Here's a yellow flower
<img src="https://huggingface.co/spaces/clip-italian/clip-italian-demo/raw/main/static/img/fiore_giallo.png" alt="drawing" width="600"/>

And here's a blu flower
<img src="https://huggingface.co/spaces/clip-italian/clip-italian-demo/raw/main/static/img/fiore_blu.png" alt="drawing" width="600"/>

### Counting
What about "one cat"?
<img src="https://huggingface.co/spaces/clip-italian/clip-italian-demo/raw/main/static/img/gatto.png" alt="drawing" width="600"/>

And what about "two cats"?
<img src="https://huggingface.co/spaces/clip-italian/clip-italian-demo/raw/main/static/img/due_gatti.png" alt="drawing" width="600"/>

### Complex Queries
Have you ever seen "two brown horses"?
<img src="https://huggingface.co/spaces/clip-italian/clip-italian-demo/raw/main/static/img/due_cavalli_marroni.png" alt="drawing" width="600"/>

And finally, here's a very nice "cat on a chair"
<img src="https://huggingface.co/spaces/clip-italian/clip-italian-demo/raw/main/static/img/gatto_su_sedia.png" alt="drawing" width="600"/>


# Broader Outlook

We believe that this model can be useful for many different applications. From image classification 
to clustering, a model like CLIP Italian can be used to support researchers and practitioners in many different tasks.
Indeed, not only it can be useful in research, but also in industry. A very interesting use-case is given by ecommerce platforms:
these website often deal with a main source of text that is the query engine and with lots of images of the products. CLIP Italian
can be a killer app in this context, providing a way to search for images and text. Nonetheless, Italy has many different collections
of photos in digital format. For example, the [Istituto Luce Cinecittà](https://it.wikipedia.org/wiki/Istituto_Luce_Cinecitt%C3%A0) is an Italian governative entity that collects photos of Italy since the
early 1900 and it is part of the largest movie studios in Europe (Cinecittà). 

# References

Scaiella, A., Croce, D., & Basili, R. (2019). [Large scale datasets for Image and Video Captioning in Italian.](http://www.ai-lc.it/IJCoL/v5n2/IJCOL_5_2_3___scaiella_et_al.pdf) IJCoL. Italian Journal of Computational Linguistics, 5(5-2), 49-60.

Sharma, P., Ding, N., Goodman, S., & Soricut, R. (2018, July). [Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning.](https://aclanthology.org/P18-1238.pdf) In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 2556-2565).

Srinivasan, K., Raman, K., Chen, J., Bendersky, M., & Najork, M. (2021). [WIT: Wikipedia-based image text dataset for multimodal multilingual machine learning](https://arxiv.org/pdf/2103.01913.pdf). arXiv preprint arXiv:2103.01913.

Reimers, N., & Gurevych, I. (2020, November). [Making Monolingual Sentence Embeddings Multilingual Using Knowledge Distillation.](https://aclanthology.org/2020.emnlp-main.365/) In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 4512-4525).

Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., & Sutskever, I. (2021). [Learning Transferable Visual Models From Natural Language Supervision.](https://arxiv.org/abs/2103.00020) ICML.

# Other Notes
This readme has been designed using resources from Flaticon.com