Industry Context: Smarter Seafood Processing

In high-volume seafood operations, misclassifying a single fish can trigger regulatory fines, customer complaints, or costly recalls. Manual inspection methods struggle to keep pace:

• Lookalike species confuse human inspectors

• Fast Lines overwhelm manual checks

• Rising Labor Costs make consistency hard to maintain

• No Traceability blocks continuous improvement

  
  

Processors need AI-powered workflows that deliver pixel-perfect accuracy, full traceability, and real-time speed. That’s where Tuba.AI comes in.

Tuba.AI: Your No-Code AI Vision Platform

Tuba.AI empowers your quality, operations, and engineering teams to design, train, and deploy semantic-segmentation workflows in minutes, without writing a single line of code. With intuitive drag-and-drop blocks, you can:

• Auto-Label with Precision: Upload images and let Tuba’s auto-labeler generate pixel-perfect segmentation masks for each fish species.

• Train Custom Segmentation Models: Choose your accuracy-vs-time preference, select PyTorch, and export in ONNX, OpenVINO, TensorRT, or quantized formats no code required.

• Deploy Anywhere: One-click deployment to edge devices on the production floor or cloud servers for centralized monitoring.

• Monitor & Retrain Continuously: Track real-time performance metrics, review alerts, and retrain workflows effortlessly as new species or lighting conditions arise.

• Integrate Seamlessly: Plug models into your existing sorting, labeling, or ERP/MES systems via standard APIs and model files.

• Scale Instantly: Extend to new species or product lines by uploading updated datasets and hitting “Retrain”—all within the same workflow.

Tuba.AI puts the power of AI vision in your hands, so you focus on compliance and quality, never infrastructure.

Use Case: Semantic Segmentation for Fish Type Detection

Tuba.AI uses semantic segmentation to automate this process by classifying every pixel in an image according to its corresponding fish type. This enables precise, fine-grained classification even in images containing multiple or overlapping fish.

In this use case, we used public Fish Type Dataset to demonstrate how Tuba.AI Workflow Builder accelerates fish species classification through AI-powered automation.

While the dataset includes only three fish species, the model and workflow can be easily extended to handle additional fish or seafood types based on visual cues. With this approach, Tuba.AI equips processing lines with the intelligence needed to maintain accuracy and compliance in high-volume operations.

Key Steps using Workflow on Tuba.AI:

1. Data Labeling:

Upload the dataset of fish images annotated with semantic segmentation masks. Each pixel is labeled by fish type, such as Black Sea Sprat,Gilt-Head Bream, or Red Mullet. This annotation format enables precise model learning. Then select the dataset type "Semantic Segmentation".

2. Model Training:

Drag and configure the Train Model block, then select the training type as Semantic Segmentation 

3. Deployment

Once the model is trained, it is deployed to either edge devices on the production floor or cloud infrastructure, enabling real-time fish species identification during processing.

This deployment step is essential to ensure the model operates reliably and efficiently at scale in a live production environment.

4. Inference and Performance Evaluation

The deployed model continuously analyzes incoming images to classify each fish by species. Real-time feedback can be integrated into sorting systems to ensure accurate labeling and routing.

Ongoing performance monitoring, combined with periodic retraining using updated datasets, ensures the model remains accurate and responsive to changes in fish appearance or environmental conditions.

5. Integration and Scalability

The fish type detection workflow can be seamlessly integrated into existing quality inspection and supply chain systems. The solution is inherently scalable and it can adapt to new classification requirements simply by extending the dataset and retraining the model. This flexibility makes it ideal for dynamic production environments and evolving regulatory standards.

Results of the Use Case

F1 Score Chart:

• The training F1 score (blue line with circles) starts at approximately 0.25 and rapidly increases to nearly 1.0, stabilizing around 0.93

• The validation F1 score (red line with circles) follows a similar pattern, reaching and maintaining close to 1.0 throughout most of the training

• Final scores shown: f1: 0.931225061416626, val_f1: 0.975519478321075A

Loss Chart:

• The training loss (orange line) starts at approximately 2.4 and decreases rapidly, stabilizing around 0.8

• The validation loss (green line) starts at about 1.2 and decreases to stabilize around 0.8

• Both loss curves show the typical decreasing pattern expected during successful model training

Business Impact of Using Tuba.AI Workflow

Why Choose Tuba.AI for building Fish Type Detection Workflows?

• Workflow Builder: Drag-and-drop blocks to assemble end-to-end segmentation pipelines

• AI for Every Team: Empower non-engineers to build and maintain AI workflows

• Fast Time to Value: Go from data to deployed model in days, not months.

• Flexible Deployment: Run models on your existing hardware edge or cloud

• Future-Proof Scalability: Extend to new species and conditions with simple dataset updates

Ready to Build Smarter Fish Type Workflows?

Accelerate your path from catch to compliance with Tuba.AI’s no-code inspection platform. Sign Up now.

Or Talk to an AI Expert today and ensure every fish is the right fish—every time..

Resources: Dataset Link : Fish Type Detection