Computer Vision Pack

The Fragmented Reality of Production Computer Vision

We built this skill because the transition from a PyTorch notebook to a production CV service is where most engineering teams bleed time. You have a model that hits 94% mAP on your validation set, but the moment you try to export it, the graph breaks. ONNX opsets conflict. Shape inference fails on dynamic axes. The inference script assumes a fixed input size and crashes on variable-resolution images. You end up writing custom middleware for every single project just to handle image preprocessing, resize logic, and response formatting.

Computer vision is not just about model architecture. It is a full pipeline: data loading, training with mixed precision, graph optimization, serialization, and low-latency serving. When you start a new project, you are forced to recreate this infrastructure from scratch. You copy-paste torch.compile calls, you guess at the right --dynamic_axes flags for ONNX, and you write yet another Django view to handle PIL.Image to numpy conversion. This is not engineering; it is maintenance debt waiting to happen. We created the Computer Vision Pack to give you a validated, multi-file workflow that handles the plumbing so you can focus on the model.

What Broken CV Pipelines Cost You

Every hour spent debugging an ONNX export is an hour your team is not spending on model accuracy or feature development. The cost of a fragmented pipeline compounds quickly. You lose credibility with product stakeholders when your "production-ready" model fails on the edge device because the tensor shapes did not match. You lose money when P99 latency spikes because the inference graph was not optimized, causing garbage collection pauses in your Python serving layer.

The complexity of modern vision tasks makes this worse. You are no longer just doing image classification. You are handling object detection and semantic segmentation, which require different output formats and post-processing steps. As noted in foundational CNN literature, moving from classification to segmentation or detection changes the entire feature hierarchy and output requirements ^[2]. If you do not structure your pipeline to handle these differences, you will ship models that return bounding boxes when you need pixel maps, or vice versa ^[3].

Furthermore, as models grow deeper, the optimization surface grows exponentially. "Going deeper with convolutions" brings richer feature hierarchies, but it also introduces more points of failure during serialization and deployment ^[5]. A 50ms latency increase in your inference loop can break real-time constraints in robotics or autonomous systems. A single misconfigured optimizer or scheduler in your training config can lead to silent convergence failure, wasting days of GPU time. We have seen teams burn thousands of dollars in cloud compute because they lacked a canonical training configuration that enforced AMP, correct scheduler warmup, and deterministic data loading.

A Logistics Team's Three-Week ONNX Detour

Imagine a team building a package sorting system for a logistics provider. They need to detect parcels and segment their contents for automated handling. They start with a standard CNN backbone ^[2]. The training goes well. They hit their target metrics. Then comes deployment.

They attempt to export the model to ONNX for the edge inference server. The export script is missing the correct dynamic axes configuration. The exported model fails shape inference when given an image with a non-standard aspect ratio. The team spends three days debugging the graph, trying different opset versions, and manually patching the export script. Meanwhile, the segmentation head is not being handled correctly by the post-processing pipeline, so the system outputs bounding boxes instead of the required pixel maps ^[3].

They eventually fix it, but the solution is fragile. Next month, they need to add a new model for damage detection. They repeat the same export struggles. They introduce a new set of bugs. This is a common pattern. Even with advanced self-supervised methods like DINOv3, which offer unprecedented pre-training capabilities for classification and segmentation, the deployment layer remains a manual, error-prone process ^[4]. The model weights are not the bottleneck; the pipeline is.

A team using a structured pipeline pack would have had an automated export script that validates the graph, a standardized inference script that handles shape inference and external data conversion, and a deployment template that routes classification, detection, and segmentation requests correctly. They would have saved those three weeks and shipped the feature on time.

What Changes Once the Pack Is Installed

Once you install the Computer Vision Pack, your workflow shifts from "fixing infrastructure" to "shipping intelligence." The skill provides a complete, validated directory structure that your AI agent can use to generate code, or that you can use as a reference for manual implementation. The outcomes are specific and measurable:

• Deterministic Exports: The scripts/export_onnx.sh script automates the ONNX export, optimization, and validation pipeline. You no longer guess at flags. The script handles model merging and function inlining, ensuring a clean graph for deployment.
• Robust Inference Logic: The templates/inference_pipeline.py template provides a production-ready inference script that handles ONNX export, shape inference, external data conversion, model merging, and function inlining. It manages the transition from tensor to response, handling the nuances of classification, detection, and segmentation outputs.
• Standardized Serving: The templates/deploy_django.py snippet provides a Django deployment template that handles image preprocessing, inference routing, and response formatting. You get a consistent API structure for all your CV models, reducing integration errors with frontend and mobile clients.
• Validated Configurations: The templates/train_config.yaml template enforces production-grade PyTorch training settings, including optimizer, scheduler, AMP, torch.compile, and data loading parameters. This eliminates configuration drift between experiments and production.
• Automated Validation: The scripts/validate_cv_project.sh script checks your project structure and validates YAML config keys. It exits non-zero on missing or malformed files, catching errors before they reach your CI/CD pipeline.

This pack integrates seamlessly with your broader ML ecosystem. If you are also handling ML model deployment with containerization and A/B testing, this pack provides the CV-specific components that the deployment pack expects. For specialized domains like medical imaging, the standardized preprocessing and export workflows ensure consistency across different imaging modalities. You can pair this with task automation to trigger retraining pipelines when data drift is detected, or use prompt engineering to generate custom model architectures within the defined constraints. When you need to pair vision with NLP text analysis for multimodal tasks, the structured output format makes integration trivial. Ensure your data pipeline is solid with an ETL pipeline to feed high-quality data into your training loops, and check implementing embedding pipeline if you need to generate vector embeddings from your vision models for search or retrieval. Finally, add AI safety guardrails to validate model outputs and prevent adversarial attacks in production.

What's in the Computer Vision Pack

This is a multi-file deliverable. Every file is designed to be used directly by an AI agent or a human engineer. There are no placeholders. There is no "todo". Here is the complete manifest:

• skill.md — Orchestrator skill that defines the CV pipeline workflow, references all templates, scripts, validators, and reference docs, and provides usage instructions for the AI agent. This is the entry point that ties the entire pack together.
• templates/train_config.yaml — Production-grade YAML configuration for PyTorch training, specifying optimizer, scheduler, AMP, torch.compile, and data loading parameters. This ensures your training runs are reproducible and optimized for hardware.
• templates/model_arch.py — PyTorch model definition template using nn.Module, nn.Dropout for training flag preservation, and compatibility with torch.compile and device placement. This gives you a solid starting point for custom architectures that are deployment-ready.
• templates/inference_pipeline.py — Production inference and optimization script handling ONNX export, shape inference, external data conversion, model merging, and function inlining. This is the core logic that runs in your serving environment.
• templates/deploy_django.py — Django deployment snippet for serving CV models, handling image preprocessing, inference routing, and response formatting for classification/detection/segmentation. This provides a consistent API surface for your models.
• references/cv-pipeline-knowledge.md — Canonical knowledge base embedding authoritative PyTorch training patterns, ONNX optimization techniques, and deployment best practices from official docs. This gives your AI agent the context it needs to make correct decisions.
• scripts/validate_cv_project.sh — Executable validator script that checks project structure, validates YAML config keys, and exits non-zero on missing or malformed files. This catches errors early in the development cycle.
• scripts/export_onnx.sh — Executable workflow script that automates the ONNX export, optimization, and validation pipeline using Python and ONNX CLI tools. This eliminates manual export errors.
• examples/worked_example.yaml — Worked example configuration for a pedestrian detection pipeline, demonstrating real-world parameter tuning and task-specific settings. This shows you exactly how to configure the pack for a real-world use case.

Install and Ship

Stop wasting weeks on ONNX exports and inference bugs. Upgrade to Pro to install the Computer Vision Pack and ship your next model with confidence. The pipeline is validated. The templates are production-ready. The only thing left is your model.