AI Algorithm Platform

In response to the needs of industries such as drug discovery, compound synthesis and surface defect detection of precision industrial products, MEGAROBO has built MegaPlant AI algorithm platform which integrates CV and NLP algorithms including advanced technologies such as small sample learning, reinforcement learning and graph calculation so as to effectively solve industry pain points with industrial-level high-precision deep learning algorithms.


Four core modules

  • Substantial CV Algorithm Library
  • Substantial NLP Algorithm Library
  • Visualization of Training and Diagramming of Reasoning
  • Open Super Parameter Adjustment

Substantial CV Algorithm Library

MegaPlant integrates over 10 deep learning algorithms in CV field, including improved and fine-tuned ResNet series, MobileNet series, YOLO series, YOLOX, FCOS, PFENet, UNet++, SOLO series, ABCNet, ViT series, etc.

MegaPlant integrates 30+ pre-training models for industrial scenarios, including display panel particle positioning model, display panel glass defect detection model, chip surface defect detection model, chip surface character detection and recognition model, wafer surface defect detection and segmentation model, cell segmentation model, cell tracking model, drug activity analysis, classification and clustering model.

Detection 1
Detection 2
Segmentation 2

Substantial NLP Algorithm Library

MegaPlant integrates nearly 10 deep learning algorithms in the fields of NLP, reinforcement learning and graph neural networks, such as improved and fine-tuned BERT, BM25, GAT, PG, 3N-MCTS, GCNN and Retro*.

MegaPlant integrates 10+ pre-training models for industrial scenarios, including chemical keyword extraction model, chemical synthesis path prediction model, chemical reaction condition prediction model, chemical reaction efficiency prediction model, etc.

Chemical Keyword Extraction
Chemical Synthesis Path Prediction
Chemical Reaction Condition Prediction
Chemical Reaction Efficiency Prediction

Visualization of Training and Diagramming of Reasoning

In MegaTE module, the parameter changes in the current training process are dynamically displayed in the form of broken lines and curves and the parameters are also configurable.

In MegaIE module, the reasoning results are displayed intuitively in the form of charts.

Open Super Parameter Adjustment

In MegaTE module, super parameters are opened and can be flexibly configured by users to meet customization requirements such as network model selection, hardware selection, data enhancement method, learning rate, proportion of training and verification samples, iteration termination conditions etc.