Artificial Intelligence Computer Vision Application
2022-07-14
Description of the Skill Competition
The 2022 BRICS Skills Competition Artificial Intelligence Computer Vision Application Competition is designed according to the actual project needs, and is designed around the development trend of artificial intelligence computer vision and its core technologies. The assessment contents include: data cleaning, data processing, data set division, model construction, model optimization, model prediction, model preservation, etc. The competition module settings include: ‘Requirements Document Analysis’, ‘Data Processing’, ‘Model Training and Prediction’, etc.
Competition module
1. Requirements Document Analysis
Model application analysis: According to the model introduction in the requirements document, describe the application scenarios of the model and the product positioning corresponding to the model.
Model construction process and precautions: Introduce the model construction process and precautions, including data collection, data processing, data loading, data set division, model training, model testing, model tuning, model saving, etc.
2. Data Processing
Image data cleaning: Use relevant digital image processing libraries to clean image datasets, such as removing abnormal images that cannot be loaded, removing single-channel images, removing duplicate image data, removing high-similarity image data, removing blurred image data, etc. Processing libraries include PIL, OpenCv, NumPy, Scikit -image, SciPy, etc.
Image preprocessing: Use related digital image processing libraries to preprocess image datasets, such as image binarization, image grayscale, image normalization, image geometric transformation, image enhancement processing, image noise reduction, etc.
Data visualization: Use visualization libraries to analyze and visualize data sets, such as drawing line charts, column charts, pie charts, etc.
3. Model Training and Prediction
Dataset loading and division: Load and divide datasets according to task requirements.
Model construction and training: Build a model based on the deep learning framework, input the training data set into the model for training, and perform parameter tuning according to the training situation to make the model more effective.
Training visualization: Use the visualization library to visualize the training process, such as changes in loss values, changes in accuracy, etc.
Model prediction: Use the trained model to predict the test data set, and calculate the accuracy, precision, recall, F1 value and other related indicators.
Model Saving: Save the trained model.
4. Professionalism
Follow rules and regulations; dress decently; behave good manner.
QQ Communication Group No. for Artificial Intelligence Computer Vision Application：686932104
Download the documentation
The 2022 BRICS Skills Competition Artificial Intelligence Computer Vision Application Competition is designed according to the actual project needs, and is designed around the development trend of artificial intelligence computer vision and its core technologies. The assessment contents include: data cleaning, data processing, data set division, model construction, model optimization, model prediction, model preservation, etc. The competition module settings include: ‘Requirements Document Analysis’, ‘Data Processing’, ‘Model Training and Prediction’, etc.
Competition module
1. Requirements Document Analysis
Model application analysis: According to the model introduction in the requirements document, describe the application scenarios of the model and the product positioning corresponding to the model.
Model construction process and precautions: Introduce the model construction process and precautions, including data collection, data processing, data loading, data set division, model training, model testing, model tuning, model saving, etc.
2. Data Processing
Image data cleaning: Use relevant digital image processing libraries to clean image datasets, such as removing abnormal images that cannot be loaded, removing single-channel images, removing duplicate image data, removing high-similarity image data, removing blurred image data, etc. Processing libraries include PIL, OpenCv, NumPy, Scikit -image, SciPy, etc.
Image preprocessing: Use related digital image processing libraries to preprocess image datasets, such as image binarization, image grayscale, image normalization, image geometric transformation, image enhancement processing, image noise reduction, etc.
Data visualization: Use visualization libraries to analyze and visualize data sets, such as drawing line charts, column charts, pie charts, etc.
3. Model Training and Prediction
Dataset loading and division: Load and divide datasets according to task requirements.
Model construction and training: Build a model based on the deep learning framework, input the training data set into the model for training, and perform parameter tuning according to the training situation to make the model more effective.
Training visualization: Use the visualization library to visualize the training process, such as changes in loss values, changes in accuracy, etc.
Model prediction: Use the trained model to predict the test data set, and calculate the accuracy, precision, recall, F1 value and other related indicators.
Model Saving: Save the trained model.
4. Professionalism
Follow rules and regulations; dress decently; behave good manner.
QQ Communication Group No. for Artificial Intelligence Computer Vision Application：686932104
Download the documentation
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