R&D Engineer in Artificial Intelligence and NLP M/F

Novelis sponsors AM Tech Day event

Novelis will be present on October 3rd at the Palais Brongniart in Paris for AM Tech Day.

Europe may have lost the battle for ownership and control of raw financial data, but it can still win the battle for control of transformed data, which represents its strategic information. This year, AM Tech Day, the flagship event of the asset management industry and its technological transformations, aims to highlight the need to address this issue at a time when generative AI is booming.

On the agenda:

  • 4 stages for captivating conferences, roundtable discussions, and workshops
  • An exhibition area bringing together event partners
  • A TV studio and 2 dynamic video studios

Immerse yourself in the world of technological innovations to optimize your investment strategies, meet regulatory requirements, and redefine your client’s experience.

In this approach, Olivier Chosson, Director of Operations at Novelis will lead a workshop at 5:10 PM on the theme of “AI and RPA at the heart of data management”.

Come and meet the Novelis teams in the event’s partner space, Stand 40!

Discover our services and book an appointment to find out more.

Top 10 great language models that have transformed NLP in the last 5 years

GPT-4, released by OpenAI in 2023, is the language model that holds one of the largest neural networks ever created, far beyond the language models that came before it. It is also the latest large multimodal model capable of processing images and text as input and producing text as output. Not only does GPT-4 outperform existing models by a considerable margin in English, but it also demonstrates great performance in other languages. GPT-4 is an even more powerful and sophisticated model than GPT-3.5, showing unparalleled performance in many NLP (natural language processing) tasks, including translation and Q&A.

In this article, we present the ten Large Language Models (LLMs) that have had a significant impact on the evolution of NLP in recent years. These models have been specifically designed to tackle various tasks in the Natural Language Processing (NLP), such as question answering, automatic summarization, text-to-code generation, and more. For each model, we have provided an overview of its strengths and weaknesses as compared to other models in its category.

A LLM (Large Language Model) model is trained on a large corpus of text data, designed to generate text like humans. The emergency of LLMs such as GPT-1 (Radford et al., 2018) and BERT (Devlin et al., 2018) was a breakthrough for artificial intelligence.

The first LLM, developed by OpenAI, is GPT-1 (Generative Pretrained Transformer) in 2018 (Radford et al., 2018). It is based on Transformer (Vaswani et al., 2017) neural network, but it has 12 layers and 768 hidden units per layer. The model was trained to predict the next token in a sequence, given the context of the previous tokens. GPT-1 is capable of performing a wide range of language tasks, including answering questions, translating text, and generating creative writing. Since it is the first LLM, GPT-1 has some limitations, for example:

  1. Bias- GPT-1 is trained on a large corpus of text data, which can introduce biases into the model;
  2. Lack of common sense: being trained from texts it has difficulties in linking knowledge to some form of understanding of the world;
  3. Limited interpretability: since it has millions of parameters, it is difficult to interpret how it makes decisions and why it generates certain outputs.

In the same year as GPT-1, Google IA introduced BERT (Bidirectional Encoder Representations from Transformers). Unlike GPT-1, BERT (Devlin et al., 2018) focused on pre-training the model on a masked language modeling task, where the model was trained to predict missing words in a sentence given the context. This approach allowed BERT to learn rich contextual representations of words, which led to improved performance on a range of NLP tasks, such as sentiment analysis and named entity recognition. BERT shares with GPT-1 some limitations, for example, the lack of common sense knowledge about the world, and the limitation in the interpretability to know how it takes decisions and the reason behind to generate some outputs. Moreover, BERT only uses a limited context to make predictions, which can result in unexpected or nonsensical outputs when the model is presented with new or unconventional information.

In the early 2019, surged the third LLM introduced by OpenAI, known as GPT-2 (Generative Pretrained Transformer 2). GPT-2 (Radford et al., 2019) was designed to generate coherent and human-like text by predicting the next word in a sentence based on the preceding words. Its architecture is based on a transformer neural network, similar to its predecessor GPT-1, which uses self-attention to process input sequences. However, GPT-2 is a significantly larger model than GPT-1, with 1.5 billion parameters compared to GPT-1’s 117 million parameters. This increased size enables GPT-2 to generate higher quality text and perform well on a wide range of natural language processing tasks. Additionally, GPT-2 can perform a wider range of tasks, such as summarization, translation, and text completion, compared to GPT-1. However, one limitation of GPT-2 is its computational requirements, which can make it difficult to train and deploy on certain hardware. Additionally, some researchers have raised concerns about the potential misuse of GPT-2 for generating fake news or misleading information, leading OpenAI to initially limit its release.

GPT-2 has been followed by other models such as XLNet and RoBERTa. XLNet (Generalized Autoregressive Pretraining for Language Understanding) was introduced by Google IA. XLNet (Yang et al., 2019) is a variant of the Transformer-based architecture. XLNet is different from traditional

Transformer-based models, such as BERT and RoBERTa, because it uses a permutation-based training method that allows the model to consider all possible word orderings in a sequence, rather than just a fixed left-to-right or right-to-left order. This approach leads to improved performance on NLP tasks such as text classification, question answering, and sentiment analysis. It has state-of-the-art results of NLP benchmark datasets, but like any other model has some limitations. For instance, it has a complex training algorithm (it uses a permutation-based training algorithm), and it needs a large amount of high-quality, diverse training data to perform well.

Simultaneously, RoBERTa (Robustly Optimized BERT Pretraining Approach) was also introduced in 2019, but by Facebook AI. RoBERTa (Liu et al., 2019) improves upon BERT by training on a larger corpus of data, dynamic masking , and training with the whole sentence, rather than just the masked tokens. These modifications lead to improved performance on a wide range of NLP tasks, such as question answering, sentiment analysis, and text classification. RoBERTa is a highly performance LLM, but it has also some limitations. For example, since RoBERTa has a large number of parameters, the inference can be slow; the model is has better proficiency in English, but it does not have the same performance in other languages.

Few months later, Salesforce Research Team released CTRL (Conditional Transformer Language Model). CTRL (Keskar et al., 2019) is designed to generate text conditioned on specific prompts or topics, allowing it to generate coherent and relevant text for specific tasks or domains. CTRL is based on a transformer neural network, similar to other large language models such as GPT-2 and BERT. However, it also includes a novel conditioning mechanism, which allows the model to be fine-tuned for specific tasks or domains. One advantage of CTRL is its ability to generate highly relevant and coherent text for specific tasks or domains, thanks to its conditioning mechanism. However, one limitation of its that it may not perform as well as more general-purpose language models on more diverse or open-ended tasks. Moreover, the conditioning mechanism used by CTRL may require additional preprocessing steps or specialized knowledge to set up effectively.

In the same month as CTRL model, NVIDIA introduced MEGATRON-LM (Shoeybi et al., 2019). MEGATRON-LM is designed to be highly efficient and scalable, enabling researchers and developers to train massive language models with billions of parameters using distributed computing techniques. Its architecture is similar to other large language models such as GPT-2 and BERT. However, Megatron-LM uses a combination of model parallelism and data parallelism to distribute the workload across multiple GPUs, allowing it to train models with up to 8 billion parameters. However, one limitation of Megatron-LM is its complexity and high computational requirements, which can make it challenging to set up and use effectively. Additionally, the distributed computing techniques used by Megatron-LM can introduce additional overhead and communication costs, which can affect training time and efficiency.

Subsequently, a few months later, Hugging Face developed a model called DistilBERT (Aurélien et al., 2019). DistilBERT is a light version of BERT model. It was designed to provide a more efficient and faster alternative to BERT, while still retaining a high level of performance on a variety of NLP tasks. The model is able to achieve up to 40% smaller model sizes and 60% faster inference times compared to BERT, without sacrificing much of its performance accuracy. DistillBERT can perform well in tasks such as sentiment analysis, question answering, and named entity recognition. However, DistillBERT does not perform as well on some NLP tasks as BERT. As well, it has been pre-trained on a smaller dataset compared to BERT, which limits its ability to transfer its knowledge to new tasks and domains.

Simultaneously, Facebook AI released BART (Denoising Autoencoder for Regularizing Translation) in June 2019. BART (Lewis et al., 2019) is a sequence-to-sequence (Seq2Seq) pre-trained model for natural language generation, translation, and comprehension. BART is a denoising autoencoder that uses a combination of denoising objectives in the pre-training. The denoising objectives help the model to learn robust representations. BART has limitations for multi-language translation, its performance can be sensitive to the choice of hyperparameters, and finding the optimal hyperparameters can be a challenge. Additionally, the autoencoder of BART has limitations, such as a lack of ability to model long-range dependencies between input and output variables.

Finally, we highlight the T5 (Transfer Learning with a Unified Text-to-Text Transformer) model, which was introduced by Google AI. T5 (Raffel et al., 2020) is a sequence to-sequence transformer-based. It uses the MSP (Masked Span Prediction) objective in the pre-training, which it consists in randomly masking spans of text with arbitrary lengths. Later, the model predicts the masked spans. Although T5 achieved results in the state of the art, T5 is designed to be a general-purpose text-to-text model, which can sometimes result in predictions that are not directly relevant to a specific task or are not in the desired format. Moreover, T5 is a large model, and it requires a high memory usage, and sometimes takes long time in the inference.

In this article, we have point out the pros and cons of the ten groundbreaking LLMs that have emerged over the last five years. We have also delved into the architectures that these models were built upon, showcasing the significant contributions they have made in advancing the NLP domain.

Novelis developed a ChatGPT connector for SS&C Blue Prism 

With the rapid advancement of technology, businesses are constantly striving to streamline their processes and minimize the resources and time required for repetitive tasks. Robotic Process Automation (RPA) has emerged as a popular solution to help achieve these goals, and Novelis, a leading system integrator company, has developed a ChatGPT connector that significantly enhances the capabilities of RPA software, particularly SS&C Blue Prism. 

How does the ChatGPT connector enhance SS&C Blue Prism? 

The ChatGPT connector, a cutting-edge technology developed by Novelis, offers SS&C Blue Prism the ability to interact with ChatGPT and leverage its advanced natural language processing capabilities. With this integration, SS&C Blue Prism can automate more complex processes that require language-based interactions, such as customer service or data analysis. By harnessing the power of ChatGPT, SS&C Blue Prism can provide faster and more accurate responses to customer inquiries, leading to increased customer satisfaction and improved business outcomes. This innovative solution allows SS&C Blue Prism to stay ahead of the curve in the rapidly evolving landscape of automation technology. 

Use Cases and Usages 

There are numerous use cases for the ChatGPT connector in SS&C Blue Prism, including: 

  1. Customer Service: With the ChatGPT connector, SS&C Blue Prism can automate customer service interactions by understanding natural language and responding appropriately. This can significantly reduce the workload for customer service agents, freeing them up to focus on more complex inquiries. 
  1. Data Analysis: ChatGPT can analyze unstructured data such as customer feedback, social media posts, or reviews, and provide insights that can be used to improve business processes. SS&C Blue Prism can use the ChatGPT connector to automate the analysis of this data, providing valuable insights in real-time. 
  1. Workflow Automation: Blue Prism can use the ChatGPT connector to automate complex workflows that require language-based interactions, such as document processing or contract management. This can significantly reduce the time and resources required for these processes, improving efficiency and productivity. 

The ChatGPT connector developed by Novelis is a valuable tool for businesses that use SS&C Blue Prism to automate their processes. By giving SS&C Blue Prism access to advanced natural language processing capabilities, businesses can streamline their workflows and improve efficiency. Whether it’s automating customer service interactions, analyzing unstructured data, or streamlining complex workflows, the ChatGPT connector is a powerful tool for businesses seeking to increase automation and reduce workload. 

About SS&C Blue Prism 

SS&C Blue Prism is the global leader in intelligent automation for the enterprise, transforming the way work is done. SS&C Blue Prism have users in over 170 countries in more than 1,800 businesses, including Fortune 500 and public sector organizations, that are creating value with new ways of working, unlocking efficiencies, and returning millions of hours of work back into their businesses. Their intelligent digital workforce is smart, secure, scalable and accessible to all; freeing up humans to re-imagine work.   

About ChatGPT  

ChatGPT is a language model developed by OpenAI. The goal is to provide quality assistance by answering questions and generating human-like responses to facilitate communication and information exchange. ChatGPT has been trained on a vast corpus of text data and has the ability to understand and respond to a wide range of topics and subjects. 

Centers of Excellence in Automation (CoE): key to the success of smart automation and a true accelerator of AI deployment

Automation Center of Excellence 

CoEs, often also called “knowledge centers”, have been used in recent years to share accumulated knowledge in different areas such as marketing, pharmaceuticals, automotive, and telecommunications. A CoE can be defined as a group of highly skilled experts who work together to analyze knowledge in a specific area of interest and provide the company with the necessary support to implement technologies in compliance with recommended best practices. 

Similarly, an Automation CoE focuses on integrating a strong framework and successful implementation of automation tools within the company. 

Benefits of the Automation Center of Excellence 

Robotic Process Automation (RPA) has become a must-have for companies looking to increase their operational performance. However, to achieve an even higher level of automation that is adaptable and scalable, intelligent automation is necessary. This is where the crucial role of automation centers of excellence (CoEs) comes in. 

CoEs enable rapid digital transformation while controlling associated risks and ensuring that automation investments are managed wisely. By establishing a CoE, companies can effectively manage and monitor their initiatives with total transparency. The automation CoE thus lies at the intersection of control, speed, and agility. 

  • Efficient robot development cycle: 

An effective Automation Center of Excellence (CoE) helps companies to centralize knowledge and learning data in the field of automation. It also provides access to best practices shared by other business units, focusing on researching RPA platforms and automation processes. This information sharing enables companies to optimize their time, speed up RPA deployment, and simplify automation-related initiative management. 

  • Integration of IT and RPA: 

A well-structured CoE ensures the participation of IT in the project team, where they were previously considered an optional addition. IT teams manage aspects such as infrastructure, security, data confidentiality, and other strategic elements from the start of a project, reducing the risk of automation disruptions. Legacy computer systems are constantly evolving and are regularly updated, which can alter automation at the user interface level. IT teams can help prepare for and anticipate these changes. 

  • Scalability Ease: 

Uncoordinated RPA projects can hinder success and prevent companies from achieving desired levels of automation and organizational objectives. A CoE is critical in preventing these types of failures and establishing a comprehensive vision for the company that allows for easy adaptation of RPA. If the goal is to implement automation throughout the organization, a CoE is essential for successful adoption and promotion of RPA or any other automation software. 

  • Improved Return on Investment (ROI): 

The absence of a CoE can lead to significant costs for integrating RPA technology, as well as difficult-to-identify inefficiencies that hinder automation, RPA acquisition, and support. A thorough evaluation of potential process automation can help avoid a negative return on investment when investing in a project. Multiple factors must be considered, and in some cases, RPA may not be the best solution for improving processes. 

CoE speeds up the deployment of AI. 

  • Deployment of AI 

In a recent study by AI experts, “64% reported that it took their organization at least a month to implement a new model, and 20% reported “6 months or more”.” 

This is where the automation center of excellence (CoE) can make a significant difference. It achieves three critical outcomes: 

  • It streamlines deployment to accelerate time-to-market. 
  • It sets the standard by determining the elements needed for a profitable business plan. 
  • It optimizes resource utilization to execute projects with increased efficiency and significantly reduced expenses. 
  • How do CoEs achieve these results? 

An effective automation CoE uses enterprise platforms and human-automation collaboration to enable rapid integration of models into workflows. This not only allows system robots to access and apply these models in real-time, but also creates conditions for continuous improvement of models using human feedback. Additionally, they drive automated extraction, transformation, quality assurance, and data management with centralized governance and compliance to standards. 

The automation CoE goes beyond just “time” considerations to achieve large-scale automation. It seamlessly integrates technology, processes, and people to deliver value-oriented business outcomes while improving operational efficiency and costs. By taking a business-oriented approach rather than simply adopting technology, it links business context to robotic process automation (RPA), AI-based technologies, process mining, and advanced analytics – and thus provides transformative results at all levels of the enterprise. This approach tackles the process fragmentation that poses a challenge to organizations. The CoE therefore shifts from the logic of automating tasks and enterprise processes to that of intelligent automation. 

Sources :  





Our best 2022 content: practices and feedback on intelligent process automation

Client testimonials, white papers, articles, webinars… Throughout the year, Novelis teams have created a lot of content to share with you the best practices and feedback on intelligent process automation. In this article, you’ll find our most popular content for 2022 to kick off 2023 and identify the levers that will boost your operational efficiency!

BLOG – White papers, articles, interview…

Anonymization of sensitive data by the combined approach of NLP and neural models: “Data exploitation is more than ever a major issue within any type of organization […] Pseudonymization/anonymization thus appears to be an indispensable technique for protecting personal data and promoting compliance with regulations.”

How can Process Intelligence tools be a springboard to your operational efficiency objective?: “The lessons learned from a Process Intelligence solution allow organizations to base their strategy for improving the operational efficiency of processes on an in-depth analysis of historical data and not only on qualitative interviews.”

[WHITE PAPER] How automation can help you overcome customer relationship challenges: “Consumer expectations have changed and customer experience has become a major differentiator, especially since its quality is increasingly measurable and comparable. […] Novelis offers you to discover the benefits in its white paper “How automation can help you overcome customer relationship challenges” divided in three parts…”

[USE CASES] RPA: Tasks with high automation potential in finance: “The digital revolution is changing the face of the financial sector, regardless of the business line: treasury, management control, accounting, finance management, etc. Transforming to innovate is becoming an obligation for these players, who must be ever faster, more reliable and more efficient in the execution of processes.”

[INTERVIEW] How APICIL Épargne decided to launch a major project to modernize, containerize and urbanize its information system: “In order to become the French leader in life insurance, APICIL Épargne decided to launch a major project to modernize, containerize and urbanize its information system. It is in this context that Novelis has been supporting APICIL Épargne for 4 years in their digital transformation on strategic subjects.”

Novelis wins Blue Prism 2022 Best AI & Cloud Innovation Solution Award with SmartRoby: “During the Partner Forum 2022 organized by Blue Prism on May 24th, Novelis has been awarded for its Automation as a Service solution SmartRoby, recognized as the best Solution of the Year in the AI & Cloud Innovation – EMEA & Global category by the leading RPA vendor.”

[USE CASES] RPA: tasks with high automation potential in insurance and for mutual: “Insurance and mutual insurance companies are facing new issues and challenges every day. RPA provides an answer to these challenges, making it a truly essential solution for these insurance and mutual organizations, which have a wide range of processes with high automation potential.”

REPLAYS – Rediscover our webinars

[Event] Novelis and NICE partners of the CX Paris All Verticals event: This CX Paris All Vertica edition will focus on the experience economy and will highlight the different levels of maturity in customer experience within different industries: banking, insurance, retail, BtoB, public services, luxury, automotive, energy… 

[Webinar] Cybersecurity: how to gain efficiency through automation?: Novelis invites you to discover how automation can become an essential operational efficiency lever for your cyber teams.

[Webinar] Customer success story CMB Monaco – Compliance and automation: the winning duo: Come and discover how to accelerate and make your compliance strategy more reliable with automation through the experience of our client CMB Monaco.

[Webinar] RPA: a solution to the challenges of the insurance industry: In this session, learn about RPA and insurance industry use cases as well as the key success factors of an automation program.

[Webinar] Accelerate your Process Automation by 30% with Process Intelligence: Discover a unified solution that combines process intelligence with automation dedicated to process exploration, optimization and monitored execution of automated processes.

Yolov7: Artificial Intelligence for real-time object detection in an image

In this article we will discover the Yolov7 model, an object detection algorithm. We will first study its use and its characteristics through a public dataset. Then we will see how to train this model ourselves from this dataset. Finally, we will train Yolov7 to identify custom objects from our own data.

What is Yolo? Why Yolov7 ?

Yolo is an algorithm for detecting objects in an image. The goal of object detection is to automatically classify, using a neural network, the presence and position of humanly identifiable objects in an image. The interest is therefore based on the capacities and performances in terms of detection, recognition and localization of the algorithms, which have multiple practical applications in the image domain. Yolo’s strength lies in its ability to perform these tasks in real time, which makes it particularly useful with video streams of tens of images per second.

YOLO is actually an acronym for “You Only Look Once”. Indeed, unlike many detection algorithms, Yolo is a neural network that evaluates the position and class of identified objects from a single end-to-end network that detects classes using a fully connected layer. Yolo therefore only needs to “see” an image once to detect the objects present, where some algorithms only detect regions of interest, before re-evaluating these to identify the classes present. Before mentioning the other versions of Yolo, it seems important here to explain the different metrics used to compare the accuracy and efficiency of a model.

Intersection over Union : IoU

Intersection over Union (literally Intersection over Union, or IoU) is a metric for measuring the accuracy of object location. As its name indicates, it is calculated from the ratio between the intersection area of the detected object and the union area of these same objects (see equation 1). By noting Adetected and Aactual the respective areas of the object detected by YOLO and the object as actually located on the image, we can then write :

Note that an IoU of 0 indicates that the 2 areas are completely distinct and that an IoU of 1 indicates that the 2 objects are perfectly superimposed. In general, an IoU > 0.5 represents a valid localization criterion.

(mean) Average Precision : mAP

Average Precision is a classification accuracy metric. It is based on the average of the correct predictions over the total predictions. So we try to get closer to a 100% mAP score (no error when determining the class of an object).

Coming back to our previous point, Yolo remains an architecture model, and not the property of a particular developer. This explains why the versions of Yolo are from different contributors. Indeed, we increment the version of Yolo (Yolov7 to date: January 2023) each time the previously mentioned metrics (especially the mAP and its associated execution time) clearly exceed the previous model and thus the state of the art. Thus, each new YolovX model is actually an improvement shown by an associated research paper published in parallel.

How does Yolo work?

Yolo works by segmenting the image that it analyzes. It will first grid the space, then perform 2 operations: localization and classification.

Figure 1: Architecture of the Yolo model, operating a grid from successive convolutions
Figure 2: Gridded image

First, Yolo identifies all the objects present with the help of frames by associating them a degree of confidence (here represented by the thickness of the box).

Figure 3: Location of objects

Then, the algorithm assigns a class to each box according to the object that it believes it has detected from the probability map.

Figure 4: Class probability map
Figure 5: Object detection

Finally, Yolo removes all unnecessary boxes using the NMS method.

NMS : Non-Maxima Suppression

The NMS method is based on a path of the high confidence index boxes, then a removal of the boxes superimposed on those by measuring the IoU. For this, we follow 4 steps. Starting from the complete list of detected boxes:

  1. Remove all boxes with a low confidence index.
  2. Identification of the box with the highest confidence index.
  3. Deleting all boxes with too large IoU (i.e. all boxes too similar to our reference box).
  4. Ignoring the reference box thus used, repeat steps 2) and 3) until all boxes in our original list have been eliminated (i.e. taking the 2nd largest confidence index box, then the 3rd, etc.).

We then obtain the following result:

Figure 6: Post-NMS output image showing the objects detected by Yolo

How to use Yolov7 with the COCO dataset ?

Now that we have seen the Yolo model in detail, we will study its use with an image database: the COCO dataset. The MICROSOFT COCO dataset (for Common Objects in COntext), more commonly called MS COCO, is a set of images representing common objects in a common context. However, unlike the usual databases used for object detection and recognition, MS COCO does not present isolated objects or scenes. Indeed, the goal when creating this dataset was to have images close to real life, in order to have a more robust training base for classical image streams, reflecting daily life.

Figure 7: Examples of isolated objects
Figure 8: Examples of isolated scenes
Figure 9: Classical scenes of everyday life.

Thus, by training our Yolov7 model with the MS COCO dataset, it is possible to obtain a recognition algorithm of nearly a hundred classes and categorizing the majority of objects, people and elements of everyday life. Finally, MS COCO is today the main reference for measuring the accuracy and efficiency of a model. To get an idea, below are the results of the different versions of Yolo.

Figure 10: Average Precision (AP) versus analysis time per image

On the x-axis, the times given to the networks to evaluate an image are indicated. The lower the time, the more we can afford to send a large flow of images to our algorithm, at the cost of accuracy. On the ordinate, the average accuracy of the models is indicated as a function of the time allowed, as seen previously.

We then notice 3 important points:

  1. Regardless of the time given to the network, Yolov7 outperforms the other Yolo models in terms of detection accuracy on the MS COCO dataset. This explains its presence as a reference in the current state of the art of real-time image-based object detection.
  2. The increase of the inference time on each image has no/few interest once the 30ms/image is exceeded. This implies that the model is more optimal on a use requiring fast image processing, such as a video stream (> 25 fps).
  3. Regardless of the model concerned, none of them exceeds 57% of detection accuracy. This implies that the model is still far from being able to be used reliably in a public setting.

To obtain the above results yourself, just follow the instructions on the GitHub page of the yolov7 model pre-trained from the MS COCO dataset: https://github.com/WongKinYiu/yolov7.

First, follow the heading :

  • Installation.

Then the sidebar:

  • Testing.

How to train Yolov7 ?

Now that we have seen how to test Yolov7 with a dataset on which it is trained, we are going to look at how we can train Yolov7 with our own dataset. We will first start a training with already prepared data, here the MS COCO dataset. Again, the Yolov7 GitHub has a specific insert for this purpose:

  • Training.

It is broken down into 2 simple steps:

  1. Download the already annotated MS COCO dataset.
  2. Launch the script ” train.py ” intrinsic to the Git directory with the dataset previously downloaded.

This one will then run on 300 steps to conform to the MS COCO dataset. It should be noted that in reality this operation has more of an instructive purpose since Yolov7 is already trained on the MS COCO dataset and thus already has an adequate model.

Prepare your own training data

Now that we have seen what Yolov7 is, how to test it and train it, we just have to provide it with our own image base to train it on our use case. We will therefore follow 4 steps to create our own dataset directly usable to train Yolov7 :

  1. Choice of our image database.
  2. Optional: Labeling of all our images.
  3. Preparation of the launch (use case of Google Collab).
  4. Training (and split operation).

To illustrate the sequence of these operations, we will take a case similar to the Novelis work used on AIDA: the detection of elements drawn on a sheet of paper.

Figure 11: Starting image: a handwritten color drawing on a sheet

To start, we will need to get a sufficient quantity of similar images. Either from our own collection, or by using a pre-existing database (for example by taking the dataset of our choice from this link. On our part, we will use the Quick Draw dataset. Once our database is formed, we will annotate our images. For that, many softwares exist, most of the time allowing to create boxes, or polygons, and to label them as a class. In our case, our database is already labeled, otherwise we would have to create a class for each element to be detected, and then identify by hand on each image the exact areas of presence of these classes. Once our dataset is labelled, we can launch a session on Google Colab and start a new Python Notebook. We will call it “MyYolov7Project.ipynb” for example.

First step: copy your dataset in your drive. In our case, we have already added to our drive a folder “Yolov7_Dataset”. Here is the tree structure of the folder:

Figure 12: Tree structure of the “Yolov7_Dataset” folder

For each folder, there is an images folder, containing the images, and a labels folder containing the associated labels generated previously. In our case, we use 20 000 images in total, including 15 000 for training, 4 000 for validation and 1 000 for testing.

The data.yaml file contains all the paths to the :

Then the characteristics of the classes:

We will not show the 345 classes in detail but they should be present in your file. We can now start our script “MyYolov7Project.ipynb” on Colab. First step, link our Drive to Colab in order to save our results (Be careful : the data of the trained network are voluminous).

Once our Drive is linked, we can now clone Yolov7 from the official Git:

By placing us in the installed folder, we check the prerequisites:

We will also need the sys and torch libraries.

We can then run the training script for our network:

Note that the batch size can be modified according to the capacities of your GPU (with the free version of Collab, 16 is the maximum possible). Don’t forget to modify your path to the “data.yaml” file according to the tree structure of your Drive. At the end of the training, we get a file with the training metrics and a trained model on our database. By launching the detection script (detect.py), we can obtain the detection result on our starting image:

Figure 13: Starting image annotated by Yolov7

As we can see, some elements were not detected (the river, the grass in the foreground) and some were mislabeled (the two mountains perceived as volcanoes, probably due to the sunlight passing by). Our model can therefore be further improved, either by refining our database or by modifying the training parameters.

Optional: Split network training (when using the free version of Google Colab)

Although our use case remains simplistic, when using the free version of Google Colab, the training of our network can take several days before being completed. However, the restrictions of Google Colab (free version) prevent a program to run more than 12 hours. To keep the training, you just have to restart it after stopping a session with our last recorded weight as a parameter (weights):

Here is an example launched with the 8th run (replace the folder “yolov78” by the last training done). You can find all your trainings in the associated folder in the Yolov7 tree.

Figure 14: Training tree. Here we are at the 12th launch

The training then resumes from the last epoch used, and allows you to progress without losing the time previously spent on your network.


Decompartmentalise feedback processing with AI

Combining IDP, RPA and AI to solve a complex process

Artificial Neural Networks for Text-to-SQL Task: State of the Art

Discover our conference paper Artificial Neural Networks for Text-to-SQL Task: State of the Art – International conference on smart Information & communication Technologies part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 684).

Thanks to the Novelis Research Team for their knowlegde and experience.


The database stores a large amount of data from all over the world, but to access this data, users must understand query languages ​​such as SQL. In order to facilitate this task and make it possible to interact with databases around the world, some research has recently emerged to deal with systems that understand natural language problems and automatically convert them into SQL queries. The purpose of this article is to provide the most advanced text-to-SQL tasks, in which we show the main models and existing solutions (natural language deal with). We also specify the experimental settings for each method, their limitations, and a comparison of the best available methods.

About the study

“Text-to-SQL task is one of the most important subtask of semantic parsing in natural language processing (NLP). It maps natural language sentences to corresponding SQL queries. In recent years, some state-of-the-art methods with Seq2Seq encoder-decoder architectures (Ilya Sutskever, Oriol Vinyals, Quoc V. Le 2014) [1] are able to obtain more than 80% exact matching accuracy on some complex text-to-SQL benchmarks such as Atis (Price, 1990; Dahl and al., 1994) [2], GeoQuery (Zelle and Mooney, 1996) [3], Restaurants (Tang and Mooney, 2000; Popescu and al., 2003) [4], Scholar (Iyer and al., 2017) [5], Academic (Li and Jagadish, 2014) [6], Yelp (Yaghmazadeh and al., 2017) [7] and WikiSQL (Zhong and al., 2017) [8].These models seem to have already solved most problems in this area. However, as (Finegan-Dollak et al., 2018) [9] show, because of the problematic task definition in the traditional datasets, most of these mod- els just learn to match semantic parsing results, rather than truly learn to understand the meanings of inputs and generalize to new programs and databases, which led to low precisions on more generic dataset as the case of Spider (YU, Tao, ZHANG, Rui, YANG, Kai, and al. 2018) [10].”

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Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 684) 

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