When computer vision technology is applied by enterprises, they usually encounter two major problems. First, how can the recognition effect continue to improve after the model goes online? Second, in the face of a large number of scattered and niche recognition needs, how to efficiently build and manage many "long tail" models.
Actual bottlenecks in model iteration
After the model is deployed, the recognition effect often declines due to the complexity and variability of the data in reality. For example, a retail company has arranged a product recognition model. However, after new packaging and new products are put on the shelves, the accuracy of the model The rate may drop quickly. If each optimization relies on algorithm experts to collect data again and adjust relevant parameters, then the entire iteration cycle will be long and the cost will be relatively high. This has become a practical obstacle to the large-scale application of technology.
Ways to deal with long-tail demand
For a large number of business identification needs that are dispersed and have a small amount of data, such as hundreds of different types of defects that need to be detected on factory assembly lines, the traditional method of developing separate models for each need is extremely inefficient. A feasible idea is to build a standardized model building process so that operators can also participate in the model customization process. By providing a large number of pre-trained basic models, only a small amount of specific data needs to be supplemented for new tasks to quickly generate a usable model.
Guided modeling that lowers barriers to entry
The key lies in the support of platform tools to realize the above ideas. Some platforms provide guided modeling interfaces, which encapsulate the steps of data annotation, parameter configuration, and training startup into visual operations. For example, users upload dozens of pictures of new products and perform frame selection and annotation. Then, they select the training task type in the interface, and the platform can automatically complete subsequent model training. This way, business personnel can also lead the initial construction of the model. This is the construction support provided by the platform.
Visual integration of business logic
The model itself cannot directly solve business problems. For example, a model that can identify "trucks" may need to trigger actions such as "forbid warehousing" within the business scope. This process is called business adaptation. With the help of visual "operators" such as dragging data, making judgments, and triggering actions, users can integrate model capabilities into specific workflows like building blocks, and then build a complete business scenario solution.
Complete closed loop from training to online
A complete process starts with data. Users upload original images and label them. These data are used to train the model. After training, the model does not go online directly, but enters the evaluation stage. The platform will use independent test data to conduct multiple rounds of evaluations on the model to ensure that its accuracy and stability meet the requirements before it can be deployed to the production environment.
Continuous evaluation and optimization mechanism
After the model goes online, the evaluation is not over yet. The system will continue to collect the recognition results of the model when it is actually used, especially the error cases detected by manual review. These "difficult samples" will be re-labeled and added to the training set to start the next round of model iterative optimization. With this closed loop, the model can continue to learn and evolve during use, step by step reducing its reliance on manual review.
