Senior Computer Vision

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:

pip install ultralytics

2. Run inference:

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")

3. Run on video:

# Process video with tracking
results = model.track("video.mp4", show=False, save=True, tracker="bytetrack.yaml")

4. Train a custom YOLO model:

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:

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

Task B: Image segmentation with SAM

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

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

# 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:

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

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"

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"

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.