computer-vision-guide
Apply computer vision research methods, models, and evaluation tools
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill computer-vision-guide --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Computer Vision Guide A skill for conducting computer vision research, covering model architectures, dataset preparation, training pipelines, evaluation metrics, and common experimental protocols for image classification, object detection, and segmentation tasks. ## Core Tasks and Architectures ### Computer Vision Task Taxonomy ``` Image Classification: Input: Single image Output: Class label(s) Models: ResNet, EfficientNet, ViT, ConvNeXt Object Detection: Input: Single image Output: Bounding boxes + class labels Models: YOLO (v5-v9), Faster R-CNN, DETR, RT-DETR Semantic Segmentation: Input: Single image Output: Per-pixel class label Models: U-Net, DeepLab, SegFormer, Mask2Former Instance Segmentation: Input: Single image Output: Per-pixel labels distinguishing individual objects Models: Mask R-CNN, Mask2Former, SAM Image Generation: Input: Text prompt or noise Output: Generated image Models: Stable Diffusion, DALL-E, Imagen ``` ### Model Architecture Evolution ``` CNNs (Convolutional Neural Networks): LeNet (1998) -> AlexNet (2012) -> VGG (2014) -> ResNet (2015) -> EfficientNet (2019) -> ConvNeXt (2022) Vision Transformers: ViT (2020) -> DeiT (2021) -> Swin Transformer (2021) ->
- Core Tasks and Architectures
- Computer Vision Task Taxonomy
- Model Architecture Evolution
- Dataset Preparation
- Building a Research Dataset
- Data Augmentation
- Training Pipeline
- Transfer Learning Workflow
- Evaluation Metrics
- Metrics by Task
- Reproducibility Checklist
- What to Report in Papers
- Ethical Considerations
What does the computer-vision-guide skill do?
Apply computer vision research methods, models, and evaluation tools
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill computer-vision-guide --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Auto-Empirical-Research-Skills, a repository with 3,244 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.