Agent skill · research science · aj-geddes
Computer Vision
Implement computer vision tasks including image classification, object detection, segmentation, and pose estimation using PyTorch and TensorFlow
Why this skill is useful
Provides specific implementations and architectures for computer vision tasks that the AI wouldn't reliably generate on its own.
What it needs
Requires opencv, seaborn, tensorflow, torch installed locally. About 5k tokens when loaded. Last updated 2026-03-04. 307 stars on the source repository.
What this skill does
Computer Vision Overview Computer vision enables machines to understand visual information from images and videos, powering applications like autonomous driving, medical imaging, and surveillance. When to Use Image classification and object recognition tasks Object detection and localization in images Semantic or instance segmentation projects Pose estimation and human activity recognition Face recognition and biometric systems Medical imaging analysis and diagnostics Computer Vision Tasks Image Classification: Categorizing images into classes Object Detection: Locating and classifying objects in images Semantic Segmentation: Pixel-level classification Instance Segmentation: Detecting individual object instances Pose Estimation: Identifying human body joints Face Recognition: Identifying individuals in images Popular Architectures Classification: ResNet, VGG, EfficientNet, Vision Transformer Detection: YOLO, Faster R-CNN, SSD, RetinaNet Segmentation: U-Net, DeepLab, Mask R-CNN Pose: OpenPose, PoseNet, HRNet Python Implementation Common CV Architectures Classification: ResNet, EfficientNet, Vision Transformer Detection: YOLO v5, Faster R-CNN, RetinaNet Segmentation: U-Net, DeepLab v3, Mask R-CNN Tracking: SORT, DeepSORT, ByteTrack Image Preprocessing Resizing to standard dimensions Normalization with ImageNet stats Data augmentation (rotation, flip, crop) Color space conversion Evaluation Metrics Classification: Accuracy, Precision, Recall, F1 Detection: mAP (mean Average Precision), IoU Segmentation: IoU, Dice coefficient, Hausdorff distance Deliverables Trained vision model Inference pipeline Performance evaluation Visualization results Model optimization report Deployment guide
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aj-geddes/computer-vision