Spot List of News Articles

We have started offering the unsupervised anomaly detection of the AI visual inspection platform "DeepAge Sensor" as an inference library (DLL for Windows) that can be integrated into inspection devices. The model, trained only on good product images, can be executed on the device without the need for defective product images in the training process. It operates solely on the CPU without requiring GPU or CUDA, and it completes all processes internally without external communication. It can be integrated using a C language interface from C/C++/C#, and it can be shipped with our company's code signing (Authenticode).

We have added a multi-stage fine-tuning feature to the AI visual inspection platform "DeepAge Sensor." You can specify the model created through fine-tuning as the base model for the next additional learning. By simply uploading additional images collected on-site, you can update the model without having to retrain it from scratch, and it can be stacked up to five times within the same lineage. In the request list, you can check the generation and lineage (source of derivation). Additionally, we have added a score graph to the verification tool "Sensor AD" to help select the decision threshold.

We have released three technical documents for the AI visual inspection platform "DeepAge Sensor" for free. The document titled "How to Launch Visual Inspection AI in a Process with No Defective Samples" (12 pages) summarizes five steps to establish anomaly detection using only good product images, along with pitfalls identified through real data validation. Additionally, we have prepared a product catalog (14 pages) and a "Guide to Integrating AI into Inspection Equipment" (13 pages) for equipment manufacturers. You can download them by providing your name, company name, and email address.

Revamped the DeepAge Sensor's Muon optimizer with Frobenius normalization and BF16 numerical contracts. Validation on 20 real data sets achieved a median maximum accuracy of 100%, with 90% accuracy reached approximately 2.6 times faster than SGD (600 batches vs 1,700 batches). Achieved 97-100% accuracy on high-difficulty data sets that could not be learned with SGD. Operation on the latest NVIDIA GPUs (Blackwell/sm_120) has also been verified.

We have started offering a desktop application called "Sensor AD" that learns the "normal standard" using only good product images and detects images that deviate from this standard as anomalies. There is no need to prepare a large number of defective product samples, allowing for the implementation of anomaly detection operations from the initial stages even in environments where collecting defective products is difficult. The inference runs on the CPU, and a GPU is not required. It features visualization through contribution heatmaps and automatic clustering of false positives.

Released Early Stopping feature for DeepAge Sensor. Automatically halts training when validation accuracy reaches the target value, reducing training time by up to half while maintaining accuracy. Efficient use of GPU resources enables training of more models in a shorter time.

Added fine-tuning (additional learning) functionality to the DeepAge Sensor. It is now possible to retrain using only additional images based on the existing model, allowing for accuracy improvements with less data and shorter time compared to retraining from scratch. The improvement cycle when new defect patterns emerge is significantly shortened.