Normal-sample modeling and anomaly localization
PatchCore, PaDiM, EfficientAD, FastFlow or STFPM → anomaly maps → threshold calibration and region merging
Image, Video & Vision Data Production · L25
Data Curation, Automatic Pre-Annotation and Hard-Sample Mining
JIVISION offers project development and technical assessment for Data Curation, Automatic Pre-Annotation and Hard-Sample Mining. Scope: Selected samples, candidate labels, difficulty scores and review tasks.
Scope and project-specific requirements
Task / output
Selected samples, candidate labels, difficulty scores and review tasks
Critical decision conditions
Difficulty does not prove label error; preserve class coverage and prevent test-data leakage during active sampling.
Candidate technical approach
These are candidate implementation paths. Establish a baseline on real samples, then select or combine methods for imaging, speed and deployment constraints. Model names do not imply measured project results.
Open-vocabulary candidates and human confirmation
Grounding DINO or open-vocabulary detection → SAM 2 candidate masks → human confirmation → fixed-class model training
Interactive segmentation and video propagation
Initial points, boxes or masks → SAM 2, XMem or temporal propagation → confidence gating and human correction
Inputs for assessment
Provide original media and usage rights, target formats/color/resolution, downstream tasks, example outputs, quality rules and sensitive-data scope.
Deliverables and interface agreement
Result schema, algorithm or processing configuration, example outputs and evaluation records for: Selected samples, candidate labels, difficulty scores and review tasks. Software format and source-code scope are agreed in the contract.
Specify input formats, output fields, coordinates/units, error states, versions and invocation methods. Define review and failure handling. The statement of work determines the exact scope of source code, executables, model files or analysis reports.
Acceptance method and measures
Task-specific acceptance measures
Candidate recall; sampled label error; review-time change
Compare before/after quality and downstream outcomes on held-out samples. Record artifacts, altered details, temporal stability, manual rework and versions.
These are measures to agree and test, not achieved-performance claims. Freeze samples, reference truth, thresholds and hardware/software versions before acceptance; report subgroup results and failures, identifying under-sampled conditions as uncovered.
Implementation and procurement stages
Define scope
Agree targets, stations, inputs and responsibilities in a statement of work, separating required and excluded conditions.
Sample validation
Use representative samples to test critical risks, document feasibility/failures and scope the next-stage estimate.
Development and integration
Implement agreed functions and interfaces with configuration/change records; use offline replay before authorized device or site integration.
Acceptance and handover
Retest against individual measures and hand over contracted artifacts with known limits; manage maintenance, expansion and changeovers as subsequent work packages.
Scope limits and licensing
Restored or generated detail is not original factual evidence. Retain originals and processing labels; permissions must cover training, derivatives, distribution and delivery.
Candidate technologies are not license clearance. Check code, model weights, training data and dependency versions separately. Replace, license or exclude components unsuitable for the intended commercial delivery. Customer data is not used for public training by default.
Project FAQs
What does this service produce?
Selected samples, candidate labels, difficulty scores and review tasks. Deliverables: Result schema, algorithm or processing configuration, example outputs and evaluation records for: Selected samples, candidate labels, difficulty scores and review tasks. Software format and source-code scope are agreed in the contract.
What needs to be confirmed first?
Difficulty does not prove label error; preserve class coverage and prevent test-data leakage during active sampling.
How is acceptance defined beyond a demonstration?
Task-specific measures: Candidate recall; sampled label error; review-time change. Compare before/after quality and downstream outcomes on held-out samples. Record artifacts, altered details, temporal stability, manual rework and versions.
How are cost and schedule assessed?
After reviewing samples for selected samples, candidate labels, difficulty scores and review tasks, equipment conditions and interfaces, scope validation, development, deployment and acceptance separately. Data coverage, site changes and delivery rights affect the estimate; no fixed performance or schedule is promised before assessment.
PROJECT INQUIRY
xuzhiyang0928@gmail.com
Discuss this service for your project
Service ID: L25 · Data Curation, Automatic Pre-Annotation and Hard-Sample Mining
Describe available samples, equipment and target measures. Agree confidentiality and permissions before transferring sensitive or personal data through an approved channel.
+86 13910119357xuzhiyang0928@gmail.com