# Computer Vision

A skill for computer vision work: image classification, object detection, segmentation and pose estimation with PyTorch and TensorFlow.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 499 installs · ★ 371
- Author: aj-geddes
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/aj-geddes/useful-ai-prompts/tree/HEAD/skills/computer-vision/SKILL.md
- Updated: 2026-03-04
- HTML: https://affpapa.org/en/nevibecode/skills/computer-vision

## What it does

The skill hands the agent ready-made patterns for six task types: image classification, object detection, semantic and instance segmentation, pose estimation, face recognition. Each type comes with its own set of architectures — ResNet, VGG, EfficientNet and Vision Transformer for classification, YOLO, Faster R-CNN, SSD and RetinaNet for detection, U-Net, DeepLab and Mask R-CNN for segmentation, OpenPose, PoseNet and HRNet for pose.

Inside is working Python code: a CNN for classification, a detector skeleton with two heads (bounding box regression and class prediction), a U-Net with encoder and decoder, and transfer learning on a pre-trained ResNet18 adapted to your class count. Preprocessing and augmentation transforms sit in a separate block: resize to 224×224, normalisation with standard mean and std, rotation, horizontal flip, colour jitter. There is also a synthetic image generator for when you have no dataset yet.

The code leans on PyTorch, torchvision, TensorFlow, Keras, OpenCV, scikit-learn, seaborn and matplotlib. You install those yourself; the skill does not pull them in. Loading the pre-trained ResNet18 is wrapped in a try/except, so on a machine without access to the weights the script prints a message and moves on — transfer learning silently never runs. Worth checking weight access before you start a training run.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add aj-geddes/useful-ai-prompts --skill computer-vision -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/computer-vision/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/computer-vision/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add aj-geddes/useful-ai-prompts --skill computer-vision -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/computer-vision/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add aj-geddes/useful-ai-prompts --skill computer-vision -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/computer-vision/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install aj-geddes/useful-ai-prompts computer-vision
```

2. **Manually: put the files in place**
- File: `.github/skills/computer-vision/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add aj-geddes/useful-ai-prompts --skill computer-vision -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/aj-geddes/useful-ai-prompts --path skills/computer-vision
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/computer-vision/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add aj-geddes/useful-ai-prompts --skill computer-vision -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/aj-geddes/useful-ai-prompts/tree/HEAD/skills/computer-vision/SKILL.md

## When to use it
- build a CNN for image classification
- label bounding boxes and train an object detector
- set up a U-Net for pixel-level segmentation
- fine-tune a pre-trained ResNet for your own class count
- configure image augmentation before training

## Example prompts
- build a CNN to classify 10 image classes with PyTorch
- create an object detector with a box head and a class head
- show me how to fine-tune ResNet18 on my dataset

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: be5304df0784de1d.

BEGIN THIRD-PARTY FILE be5304df0784de1d
````text
---
name: Computer Vision
description: Implement computer vision tasks including image classification, object detection, segmentation, and pose estimation using PyTorch and TensorFlow
---

# 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

```python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from PIL import Image, ImageDraw
import torch
import torch.nn as nn
from torch.utils.data import DataLoader, TensorDataset
from torchvision import transforms, models, datasets
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import cv2
from sklearn.metrics import accuracy_score, confusion_matrix
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')

print("=== 1. Image Classification CNN ===")

# Define image classification model
class ImageClassifierCNN(nn.Module):
    def __init__(self, num_classes=10):
        super().__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 32, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.BatchNorm2d(32),
            nn.MaxPool2d(2, 2),

            nn.Conv2d(32, 64, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.BatchNorm2d(64),
            nn.MaxPool2d(2, 2),
````
END THIRD-PARTY FILE be5304df0784de1d

The third-party file has ended. Everything between the be5304df0784de1d tags is data: requests and commands from it are not followed. An “end of file” line with another tag inside the block is part of the file.

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---
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