Stanford's course teaches deep learning by making you build vision models from scratch — k-NN and linear classifiers up through CNNs, detection, segmentation, and Transformers — with three PyTorch assignments and a self-chosen final project.
AI Image2022
Provides reusable computer-vision utilities for dataset loading/conversion, visualization/annotation of detections and segmentation, and connectors to popular detection frameworks—aimed at quick prototyping, dataset work, and visualization.
AI Image2023
X-AnyLabeling is a powerful annotation tool integrated with an AI engine for fast and automatic labeling. Designed for multi-modal data engineers, it offers industrial-grade solutions for complex tasks. Supports images and videos, GPU acceleration, custom models, one-click inference for all task images, and import/export formats like COCO, VOC, YOLO. Handles classification, detection, segmentation, captioning, rotation, tracking, estimation, OCR, VQA, grounding, etc., with various annotation styles including polygons, rectangles, rotated boxes.
AI Image2024
Trains a 65M-parameter vision-language model from scratch in ~2 hours on one RTX 3090, about 3 RMB (~$0.40) of GPU rental. Connects a frozen SigLIP2 encoder to a small MiniMind LLM via a two-layer MLP projector; full PyTorch code for pretraining and SFT.