You will get a custom computer vision proof of concept for your images or videos
Project details
You will get a custom computer vision solution built around your real images or videos—not a generic demo or one-size-fits-all script. I can develop a focused workflow for
classification, detection, segmentation, anomaly inspection, visual search, tracking, counting, video understanding, or vision-language adaptation.
My research and engineering background includes a CVPR 2026 workshop paper on robust vision-language models, work on vision foundation models, and large-scale PyTorch video
pipelines used with 2.3 million videos. I combine current research with practical engineering to choose the simplest approach that can meet your goal.
Each project starts with a clear success metric and representative data. You receive reproducible code, measured evaluation, sample outputs, error analysis, and honest
limitations. Depending on the selected package, I can also provide trained weights, a batch or video inference pipeline, a FastAPI endpoint, and Docker packaging. The
result is a testable prototype that your team can understand, integrate, and improve.
classification, detection, segmentation, anomaly inspection, visual search, tracking, counting, video understanding, or vision-language adaptation.
My research and engineering background includes a CVPR 2026 workshop paper on robust vision-language models, work on vision foundation models, and large-scale PyTorch video
pipelines used with 2.3 million videos. I combine current research with practical engineering to choose the simplest approach that can meet your goal.
Each project starts with a clear success metric and representative data. You receive reproducible code, measured evaluation, sample outputs, error analysis, and honest
limitations. Depending on the selected package, I can also provide trained weights, a batch or video inference pipeline, a FastAPI endpoint, and Docker packaging. The
result is a testable prototype that your team can understand, integrate, and improve.
Machine Learning Tools
BERT, ChatGPT, Google AutoML, MLflow, NLTK, NumPy, OpenCV, Python, Python Scikit-Learn, PyTorch, SQL, Word2vec, XGBoostWhat's included
| Service Tiers |
Starter
$200
|
Standard
$600
|
Advanced
$1,200
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 15 days |
Number of Revisions | 1 | 2 | 2 |
Number of Model Variations | 1 | 1 | 2 |
Number of Scenarios | 1 | 1 | 1 |
Number of Graphs/Charts | 3 | 5 | 7 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | |||
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$50 - $250
Additional Model Variation
(+ 3 Days)
+$100Frequently asked questions
About Linxiang
Computer Vision Engineer | PyTorch, Multimodal AI
Szeged, Hungary - 2:50 am local time
If you have data and a clear business use case, I can help you turn it into a measurable proof of concept for image classification, object detection, segmentation, or video analysis.
What I can deliver:
• Training and fine-tuning of computer vision models
• Precision/recall evaluation and false-positive/false-negative analysis
• Image, batch, and video inference pipelines
• Clean PyTorch and OpenCV code with documentation
• FastAPI endpoints and Dockerized prototypes
• Training speed, memory, and reproducibility improvements
My background combines computer vision research with large-scale engineering. I have worked on distributed PyTorch training over 2.3 million videos, accelerated training and evaluation workflows by more than 10Ă— through caching, and contributed to research on vision-language models and video understanding. My work has also been accepted at top AI/CV conference.
I focus on clear scope, measurable results, reproducible code, and honest reporting of limitations.
Steps for completing your project
After purchasing the project, send requirements so Linxiang can start the project.
Delivery time starts when Linxiang receives requirements from you.
Linxiang works on your project following the steps below.
Revisions may occur after the delivery date.
Scope and data validation
I review the objective, sample data, labels, success metric, and package scope. I identify data risks, missing inputs, and any constraints that may affect the solution.
Baseline and solution design
I select an appropriate model and evaluation protocol, prepare the data pipeline, and establish a reproducible baseline before deeper development or tuning.