Model Evaluation Jobs

I’m looking for an **expert Computer Vision / YOLO specialist** to optimize my existing object detection model. I already have: * PC with NVIDIA GPU * Dataset and annotations * Training environment/setup * Existing YOLO model and baseline results ### What I need I need someone experienced who can **analyze the dataset and model, experiment with training parameters, augmentations, image size, batch size, learning rate, model configuration, etc., and achieve the best possible results.** This is **not just a training job**. I want someone who can analyze false positives/negatives, identify weaknesses, run experiments, compare results, and intelligently tune the model. Remote access to my PC will be provided. **Experience with YOLO, PyTorch, object detection, small-object/drone de...

Project Overview: I am looking for a freelancer to develop a Heart Disease Prediction Analysis project using Python and Machine Learning. Requirements: Perform data cleaning and preprocessing Handle missing values and duplicates Perform Exploratory Data Analysis (EDA) Create meaningful visualizations using Matplotlib and Seaborn Analyze correlations and important features Build and compare at least 3 classification models: Logistic Regression Decision Tree Random Forest Evaluate models using: Accuracy Precision Recall F1-Score Confusion Matrix ROC-AUC Include a sample prediction using the best-performing model Provide a clear final conclusion and model comparison Deliverables: Complete Jupyter Notebook (.ipynb) Clean and commented Python code EDA visualizations Machine learning models...

I’m running a systematic investigation into how prompt phrasing influences in-context regression performance. The work spans six separate axes, and for this engagement I want you to concentrate on two of them—Data preprocessing and Evaluation metrics—while keeping the other axes in mind so our findings remain extensible. The core set-up centres on text-based prompts only; no visual or multimodal inputs will appear in this round. All experiments will target polynomial regression tasks, so the prompts, data splits, and metric choices should reflect the non-linear nature of the underlying relationships. Here is what I need from you: • Curate or generate a clean, well-documented dataset suitable for polynomial regression, then outline the preprocessing steps you apply...

I have a private collection of images that I want to sort automatically, and I need your expertise to build and fine-tune an image-classification pipeline. The dataset is purely visual—no accompanying text or numerical metadata—so the focus is on extracting meaningful visual features and assigning each image to the correct category. My end goal is straightforward: a reliable model that can classify every new photo I add, with clear metrics that show its accuracy. I haven’t fixed the exact number of categories yet; we can review the sample set together and decide what makes sense. You are free to work in Python with libraries such as TensorFlow, Keras or PyTorch, and any supportive tools for preprocessing, augmentation, and evaluation. I’ll share the image folder...