DINOv3 beats CNNs? I tested it
Meta has released DINOv3 🚀, the successor to DINOv2, and it is already proving to be one of the most powerful vision backbone models available today. In this video, I test DINOv3 on the Birds 200 Species 🐦 Image Classification dataset and compare its performance against DINOv2.
With frozen DINOv3 weights and a simple linear classification head, the model reached 96% ✅ accuracy, which is a huge jump from the 86% I achieved with DINOv2.
What you will see in this video:
🔥 Setting up the dataset and environment with Docker + notebooks
📥 How to download and use the gated weights from Hugging Face
⚡ Step by step training with the DINOv3 backbone
📊 Training and evaluation results in real time
🏆 Why DINOv3 is setting a new benchmark for image classification
Key results:
DINOv2 (embedding model): ~86% accuracy
DINOv3 (base weights + linear head): ~96% accuracy 🚀
Github Repository: https://github.com/algofly-oss/dinov3...
This repo is fully reproducible. Just clone it, run `docker compose up`, and you are ready to go. The GitHub link is in the description.
DINOv3 is already changing the game for Vision AI and will be used far beyond classification. In future videos I will test it on more advanced tasks.
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#DINOv3 #VisionAI #ImageClassification #DeepLearning #MetaAI