Vertex AI Workbench with NVIDIA T4/A100
The Computer Vision Lab is where you train machines to see. It combines GPU compute for model training with a suite of annotation, visualization, and dataset management tools purpose-built for image and video data. Datasets are stored on Cloud Storage and streamed efficiently to your GPU for training — no downloading gigabytes of images to local disk. The lab comes pre-loaded with benchmark datasets (COCO, ImageNet subsets, custom annotated datasets) and state-of-the-art model implementations (YOLOv8, Detectron2, SAM). Label Studio and CVAT are available for hands-on annotation exercises.
Start the lab. A T4 GPU instance with OpenCV, YOLO, and Detectron2 pre-installed launches. Datasets load from Cloud Storage.
For annotation exercises, open Label Studio in your browser. Draw bounding boxes, segment objects, or classify images.
Load annotated data into your training pipeline. Train YOLO, Mask R-CNN, or custom architectures with GPU acceleration.
Run COCO-style evaluation. Review per-class mAP, visualize predictions vs ground truth, and identify failure cases.
Render detection/segmentation overlays on images and video. Build OpenCV visualization pipelines for demo outputs.
Save trained models. Export for edge deployment exercises or push to model registry.
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Explore lab →Enroll in a course that uses this lab, or visit our Houston center for a hands-on demo.