AI Implementations

We employ different purpose ML / DL / AI models that are suitable for your use cases.

1

Use Cases

Any automation we can think of is possible in a powerful, natural, straightforward, modern way with our recommended models and AI/ML/DL methodology.
2

Specification

Project specifications in clear terms.
3

Project Planning

From specifications to actual implementation, assets, time management.
4

Deployment

From staging to production of the actual implementation.
5

Maintenance

From environment tuning to application maintenance and continuous update.
Example Use Case in Automation with AI Model
Automatic text description, properties and classification of warehouse products.

Midjourney Describe, CLIP Interrogator Locally, Image to Prompt via Replicate, MM-ReAct, and Scenex by Jina AI are all intriguing approaches. Before selecting a tool, we should consider their specific requirements.

Midjourney Describe provides quick results and a good understanding of visual concepts but does not have an API.

Local CLIP Interrogator has an API and understands visual concepts well but struggles with abstract concepts. CLIP Interrogator is similar to Image to Prompt via Replicate but only supports one model.

MM-ReAct understands visual concepts well, recognizes texts and abstract shapes well, and is fast but heavily reliant on external services.

Scenex by Jina AI is a commercial product that understands visual concepts well, recognizes geometric shapes, and responds quickly, but lacks the precision of MM-React or Midjourney Describe.

As image-to-text research advances, the prominence of conversational multi-modal AI will necessitate even more outstanding performance and precision.

Alternatives such as EVA-CLIP and GenerativeImage2Text provide promising solutions to these problems.

Lightweigth versions for modile/edge AI. Choices for tasks such as image classification, object detection, and semantic segmentation on resource-constrained devices.

SqueezeNet:
Pros: Achieves good accuracy with fewer parameters. Employs 1x1 convolutions to reduce model size.
Cons: May not perform as well as larger models on more complex tasks.
ShuffleNet:
Pros: Utilizes channel shuffling to reduce computational cost. Good balance between accuracy and efficiency.
Cons: Requires careful implementation of channel shuffling, and may not be as straightforward to use as MobileNet.
EfficientNet:
Pros: Uses a compound scaling method to balance model size, accuracy, and efficiency. Generally achieves higher accuracy compared to MobileNet on the same computational budget.
Cons: Can be computationally more intensive than MobileNet.
MobileNet:
Pros: Specifically designed for mobile and edge devices, balancing accuracy and efficiency. Well-suited for real-time applications with limited resources.
Cons: May sacrifice some accuracy compared to larger models.

Choosing the right model depends on your specific use case, available resources, and performance requirements. MobileNet is often a solid choice for resource-constrained environments, while EfficientNet may be preferred when computational resources are less restricted and higher accuracy is crucial. SqueezeNet and ShuffleNet can be considered when striking a balance between efficiency and accuracy is a priority.

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