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senior-computer-vision

Object detection, image segmentation, and visual AI. Use when a user asks to detect objects in images, segment images, run YOLO inference, build a computer vision pipeline, use Faster R-CNN or SAM, deploy a vision model with TensorRT, or perform image classification. Covers YOLO, SAM, Mask R-CNN, and TensorRT optimization.

#computer-vision#yolo#segmentation#detection#tensorrt
terminal-skillsv1.0.0
Works with:claude-codeopenai-codexgemini-clicursor
Source

Usage

$
✓ Installed senior-computer-vision v1.0.0

Getting Started

  1. Install the skill using the command above
  2. Open your AI coding agent (Claude Code, Codex, Gemini CLI, or Cursor)
  3. Reference the skill in your prompt
  4. The AI will use the skill's capabilities automatically

Example Prompts

  • "Analyze the sales data in revenue.csv and identify trends"
  • "Create a visualization comparing Q1 vs Q2 performance metrics"

Documentation

Overview

Build and deploy computer vision pipelines for object detection, image segmentation, and visual AI. Supports YOLO (v8/v11), Faster R-CNN, SAM (Segment Anything Model), and Mask R-CNN. Includes TensorRT optimization for production deployment with real-time inference.

Instructions

When a user asks for computer vision help, determine the task:

Task A: Object detection with YOLO

  1. Install ultralytics:
bash
pip install ultralytics
  1. Run inference:
python
from ultralytics import YOLO

# Load a pretrained model
model = YOLO("yolo11n.pt")  # nano (fastest) | s | m | l | x (most accurate)

# Detect objects in an image
results = model("image.jpg")

# Process results
for result in results:
    boxes = result.boxes
    for box in boxes:
        cls = int(box.cls[0])
        conf = float(box.conf[0])
        label = model.names[cls]
        x1, y1, x2, y2 = box.xyxy[0].tolist()
        print(f"{label}: {conf:.2f} at [{x1:.0f},{y1:.0f},{x2:.0f},{y2:.0f}]")

# Save annotated image
results[0].save("result.jpg")
  1. Run on video:
python
# Process video with tracking
results = model.track("video.mp4", show=False, save=True, tracker="bytetrack.yaml")
  1. Train a custom YOLO model:
python
model = YOLO("yolo11n.pt")
model.train(
    data="dataset.yaml",   # Path to dataset config
    epochs=100,
    imgsz=640,
    batch=16,
    device=0,              # GPU index
    patience=20,           # Early stopping
)

Dataset YAML format:

yaml
path: ./dataset
train: images/train
val: images/val
names:
  0: cat
  1: dog
  2: bird

Task B: Image segmentation with SAM

python
from segment_anything import sam_model_registry, SamPredictor, SamAutomaticMaskGenerator

# Load SAM model
sam = sam_model_registry["vit_h"](checkpoint="sam_vit_h_4b8939.pth")
sam.to(device="cuda")

# Point-based segmentation
predictor = SamPredictor(sam)
predictor.set_image(image)  # numpy array (H, W, 3)

# Segment with a point prompt
masks, scores, logits = predictor.predict(
    point_coords=np.array([[500, 375]]),  # (x, y) coordinates
    point_labels=np.array([1]),            # 1 = foreground, 0 = background
    multimask_output=True,
)

# Automatic mask generation (segment everything)
mask_generator = SamAutomaticMaskGenerator(sam)
masks = mask_generator.generate(image)
# Each mask: {segmentation, area, bbox, predicted_iou, stability_score}
print(f"Found {len(masks)} segments")

Task C: Faster R-CNN and Mask R-CNN with torchvision

python
import torchvision
from torchvision.models.detection import fasterrcnn_resnet50_fpn_v2, maskrcnn_resnet50_fpn_v2
from torchvision.transforms import functional as F
from PIL import Image

# Object detection with Faster R-CNN
det_model = fasterrcnn_resnet50_fpn_v2(weights="DEFAULT")
det_model.eval().cuda()

img = Image.open("image.jpg")
img_tensor = F.to_tensor(img).unsqueeze(0).cuda()

with torch.no_grad():
    predictions = det_model(img_tensor)[0]

# Filter by confidence
threshold = 0.7
for i in range(len(predictions["scores"])):
    if predictions["scores"][i] > threshold:
        label = predictions["labels"][i].item()
        score = predictions["scores"][i].item()
        box = predictions["boxes"][i].tolist()
        print(f"Class {label}: {score:.2f} at {box}")

# Instance segmentation with Mask R-CNN
seg_model = maskrcnn_resnet50_fpn_v2(weights="DEFAULT")
seg_model.eval().cuda()

with torch.no_grad():
    predictions = seg_model(img_tensor)[0]
    # predictions["masks"] contains per-instance binary masks

Task D: TensorRT optimization for deployment

python
# Export YOLO to TensorRT
from ultralytics import YOLO

model = YOLO("yolo11n.pt")
model.export(format="engine", device=0, half=True)  # Creates yolo11n.engine

# Run inference with TensorRT engine
trt_model = YOLO("yolo11n.engine")
results = trt_model("image.jpg")

For custom models:

python
import tensorrt as trt
import torch

# Export PyTorch model to ONNX first
torch.onnx.export(
    model, dummy_input, "model.onnx",
    opset_version=17,
    input_names=["input"],
    output_names=["output"],
    dynamic_axes={"input": {0: "batch"}, "output": {0: "batch"}},
)

# Convert ONNX to TensorRT
# trtexec --onnx=model.onnx --saveEngine=model.engine --fp16

Task E: Image classification

python
from torchvision.models import efficientnet_v2_s, EfficientNet_V2_S_Weights
from torchvision.transforms import functional as F
from PIL import Image

weights = EfficientNet_V2_S_Weights.DEFAULT
model = efficientnet_v2_s(weights=weights).eval().cuda()
preprocess = weights.transforms()

img = Image.open("image.jpg")
batch = preprocess(img).unsqueeze(0).cuda()

with torch.no_grad():
    logits = model(batch)
    probs = torch.softmax(logits, dim=1)[0]
    top5 = torch.topk(probs, 5)

categories = weights.meta["categories"]
for score, idx in zip(top5.values, top5.indices):
    print(f"{categories[idx]}: {score:.2%}")

Examples

Example 1: Count products on a shelf

User request: "Count how many bottles are on each shelf in this image"

python
model = YOLO("yolo11m.pt")
results = model("shelf.jpg", conf=0.5)
bottles = [b for b in results[0].boxes if model.names[int(b.cls[0])] == "bottle"]
print(f"Detected {len(bottles)} bottles")
results[0].save("shelf_annotated.jpg")

Example 2: Segment and extract a foreground object

User request: "Remove the background from this product photo"

Use SAM with a center-point prompt to segment the main object, then apply the mask to create a transparent PNG background.

Example 3: Real-time detection on a webcam

User request: "Run object detection on my webcam feed"

python
model = YOLO("yolo11n.pt")
results = model(source=0, show=True, conf=0.5)  # source=0 for webcam

Guidelines

  • Start with the smallest model variant (nano/small) and scale up only if accuracy is insufficient.
  • Use TensorRT or ONNX Runtime for production deployments; they provide 2-5x speedup over PyTorch.
  • For custom detection tasks, fine-tune YOLO on your dataset rather than training from scratch.
  • Set confidence thresholds based on the application: 0.5 for general use, 0.7+ for high-precision needs.
  • Use half-precision (FP16) inference on GPUs for nearly 2x speedup with minimal accuracy loss.
  • Pre-process images to the model's expected resolution before inference for best results.
  • For video processing, use batch inference and tracking (ByteTrack) for temporal consistency.
  • Benchmark inference speed with model.benchmark() (YOLO) before committing to a model size.

Information

Version
1.0.0
Author
terminal-skills
Category
Data & AI
License
Apache-2.0

Use Cases

→Build a Production Computer Vision Pipeline with AI-Generated Training Data