Fine tuning…the Ferrari…

Fine-tuning the details

Fine-tuning the strokes! 🎶 #WhistlePodu #Yellove

fine tuning rotoscope ai video asset for the next 1/1

RF-DETR converges faster than other detectors RF-DETR-B after 10... epochs of fine-tuning vs. YOLO11m after 30 epochs of fine-tuning link:show more

Still fine tuning the never miss system😶 #pixelart #ドット絵... #art #indiegame #indiedevshow more

Fine tuning the camper for maximum RAAHHH 🦅🇺🇸 Connor... Zilisch | Kampgrounds of America (KOA)show more

Reinforcement Learning (RL) has long been the dominant method... for fine-tuning, powering many state-of-the-art LLMs. Methods like PPO and GRPO explore in action space. But can we instead explore directly in parameter space? YES we can. We propose a scalable framework for full-parameter fine-tuning using Evolution Strategies (ES). By skipping gradients and optimizing directly in parameter space, ES achieves more accurate, efficient, and stable fine-tuning. Paper: Code:show more

Fine-tuning cave level's background assets, light and atmosphere ✨... #WishlistWednesday #pixelart #metroidvania #indiedevshow more

After some fine-tuning and adjustments, we’ve finally got #autonomous... #JohnDeere rolling! #plant25 #AgX #AgTwittershow more

Reminder to start your games small! You can always... waste 6 months fine tuning coin animations later...show more

Fine tuning for release.. I GOT to get this... right 🫶Thanks as always for your support #SuperPolygonGrandPrixshow more

What if your model handled its own tuning? ⚙️... With NVIDIA TAO 7, just prompt your coding agent what you want in plain language and let it do the rest. • Agent skills plug into your coding agent and improve accuracy. • AutoML removes the guesswork from hyperparameters. LLM-guided tuning finds strong configs up to 2x faster. • Fine-tune any Hugging Face CV or VLM model on local NVIDIA GPUs. • Data enhanced fine-tuning helps your agent identify why the model fails, then fix it. Try TAO skills ➡️show more

Always wondered what is that mysterious sound before take... off? It's the Airbus A320 adjusting its horizontal stabilizer fine-tuning the balance for a perfect climb. 🎥Credit: Aviationtrack on Instagramshow more

Our Facescanner just stepped up a level! ⬆️ We’ve... been hard at work fine-tuning our setup for even greater detail 🤓 Showcase video on the way soon 👀show more

Asking the #Ferrari296Speciale to describe driving thrills. #Driving Ferrari #Ferrari

Full Fine-tuning vs. Freezing Layers. Interact 👉 and ==... Full Fine-tuning == A real network has many — three layers in this example, billions of parameters in a production model. What does fine-tuning look like when you update all of them? That’s full fine-tuning: continue training every weight in the pretrained network on your new task. Every layer’s W gets its own ΔW. Nothing is frozen — every parameter is in play. Think of an MLP as a chain of prerequisites leading to an advanced course. Layer 1 might be Linear Algebra, layer 2 Probability, layer 3 Advanced Machine Learning — each one building on what came before. Fine-tuning is what happens during graduate study: the foundations are already there from undergrad, so you’re not re-learning. Full fine-tuning is reviewing every prerequisite to see what new topics have appeared and what discoveries the field has made since the last time you sat through them. Effective — but exhausting. This diagram shows the same three-layer MLP twice, side by side. On the left, the pretrained network runs on input X: three weight matrices W₁, W₂, W₃, each followed by a ReLU activation. Full fine-tuning gives the model the most freedom to specialize. Every parameter can move — and every parameter that can move must be stored. But not every prerequisite needs revisiting. The further you go back in the chain, the less the material has changed since pretraining — the linear-algebra basics under your computer-vision course are largely the same as they ever were. The next page does exactly that: freeze the prerequisites that haven’t moved, and only refresh the advanced one closest to your specialization. == Freezing Layers == Full fine-tuning reviewed every prerequisite — Linear Algebra, Probability, Advanced ML — to refresh each subject with the latest topics. Effective, but exhausting. Then you realize something. The prerequisites haven’t actually changed that much. Linear Algebra is still Linear Algebra; the matrix decompositions you learned still hold. Probability is still Probability; the distributions and Bayes’ rule haven’t moved. Almost all the new material — the new ideas, the recent discoveries — lives in the advanced layer at the top. That’s freezing layers: keep the prerequisite layers fixed at their pretrained state, and only update the advanced one. In the diagram below, W1​ and W2​ — the foundational prerequisites — stay frozen. Only W3​ — the layer closest to your task-specific output — gets a ΔW.show more