Boost Game Detection: Custom Prompts For Higher Confidence

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Hey guys! Let's dive into a super important topic for anyone working on game detection: improving detection confidence using game-specific prompts. If you're using generic prompts and seeing low confidence scores, you're in the right place. We'll explore why this happens, how to fix it, and how much of a difference it can make in your projects.

The Problem with Generic Prompts

So, why are generic prompts a problem? Well, they're just not specific enough to help the model accurately identify objects within a game. Think about it: the difference between "enemy soldier with rifle" and "orc warrior with sword and shield" is huge when you're trying to spot enemies in different game worlds. That's the problem. Generic prompts, while they might work sometimes, often lead to low confidence scores and missed detections. If the model isn't confident, it will give poor results. Let's look at some examples:

  • "Enemy soldier with rifle" - This is too broad. It could apply to many games, which doesn't give the model enough context.
  • "Armored enemy trooper" - Still pretty general. What kind of armor? What game? The model needs more cues.
  • "[Enemy] humanoid stone golem" - Better, because of the "golem" part, but still generic. It lacks the specific game's visual style or character design.

These generic prompts are one of the main reasons for low detection confidence and missed targets. If you're seeing a low confidence threshold (like CONF_TH = 0.10), it's a strong indicator that your model isn't very confident with the current prompts. This can lead to a frustrating experience where detections are inconsistent. The primary goal is to give the model the best possible chance to detect what you want it to detect.

Game-Specific Optimization Strategies: Leveling Up Your Prompts

Let's get into the good stuff: how to create prompts that are actually effective. The core idea is to move away from generic terms and embrace the unique visual language of your game. Below is a process to tailor your prompts, which can dramatically improve your detection accuracy.

1. Analyze Your Game's Visual Style: Know Your Game

Before you start writing prompts, you need to understand your game's visual DNA. Think of it like this: you're trying to teach the model to recognize something very specific. So, the more information you give it, the better. Here's what to consider:

  • Art Style: Is your game realistic, cartoonish, pixelated, or something else? Is it 2D or 3D? The art style is critical, because it determines how objects look. If it's a realistic style, the descriptions can be highly detailed. If it's a cartoon style, the descriptions will be a bit more stylized.
  • Setting: Is it set in modern warfare, a fantasy world, a sci-fi universe, or historical times? The setting will define what kinds of enemies, weapons, and environments you'll encounter. A game set in space will look nothing like a medieval fantasy game.
  • Enemy Types: Does your game feature humans, robots, monsters, vehicles, or a mix? Knowing the enemy types helps you narrow down the descriptions. Knowing the enemy types is very important to ensure the correct prompts are used. The prompts for a human will be different from those for a robot.
  • Visual Characteristics: Pay close attention to colors, shapes, and distinctive features. Maybe there's a particular color scheme, a unique shape for weapons, or a specific feature that makes enemies stand out. Think about it like this: what makes the enemies in your game unique?

Thoroughly analyzing these aspects will give you a solid foundation for crafting effective prompts. Take screenshots, watch gameplay videos, and really immerse yourself in the visual world of your game. By understanding these core components, you will be able to generate prompts that the model is much more likely to understand.

2. Create Specific Prompts: Go Beyond the Basics

Once you've analyzed your game's visual style, it's time to create specific prompts. The goal is to move beyond the generic terms and describe enemies in a way that aligns with your game's unique characteristics. Here are some examples to get you started:

For Modern FPS Games:

  • "Soldier in military uniform with assault rifle"
  • "Enemy combatant in tactical gear"
  • "Hostile player with weapon drawn"
  • "Armed opponent in camouflage"

For Fantasy Games:

  • "Orc warrior with sword and shield"
  • "Skeleton archer with bow"
  • "Goblin with dagger"
  • "Undead zombie shambling"

For Sci-Fi Games:

  • "Robot soldier with plasma rifle"
  • "Alien creature with energy weapon"
  • "Cyborg enemy with mechanical limbs"
  • "Hostile android with laser gun"

Notice how these prompts use the keywords and descriptions specific to each game type. "Soldier" becomes "Orc Warrior", and "soldier" becomes "Robot Soldier." These are more descriptive and targeted, increasing the likelihood of accurate detections.

3. Include Visual Context: Paint a Detailed Picture

Take your prompts a step further by including visual context. This means adding environmental and stylistic descriptors to your prompts. Think of it as giving the model more clues to help it recognize what it's seeing. Here's how you can do it:

  • "Enemy soldier in [game art style] rendered in [lighting conditions]" - This tells the model not just what the enemy is, but also how it looks within the game's style and the current lighting.
  • "Hostile [character type] from [game name] with [distinctive features]" - This gives the model the context of the game itself, as well as unique identifiers for enemies. This is incredibly important.

By adding this level of detail, you're giving the model a more comprehensive understanding of the game's visuals.

4. Test and Iterate: The Key to Success

Crafting good prompts isn't a one-and-done process. You'll need to test your prompts, monitor their performance, and refine them over time. Here's a step-by-step approach:

  1. Start with a Few Prompts: Begin with 3-5 specific prompts tailored to your game.
  2. Monitor Confidence Scores: Watch the confidence scores in real-time as you play. Are they consistently high (0.4+)? Or low (0.1-0.3)?
  3. Add Prompts for Missed Detections: If you're missing enemies, add prompts that describe the specific types of enemies you're missing.
  4. Remove Low-Confidence Prompts: If some prompts consistently show low confidence, remove them or refine them.

Iteration is an important part of the process. It allows you to find the prompts that work best for your game and adjust them as needed. Continuously testing and refining your prompts will lead to better results and detection accuracy.

5. Confidence Threshold Tuning: Fine-Tuning for Optimal Performance

With better prompts, you should be able to increase the confidence threshold (CONF_TH). The confidence threshold is the minimum confidence score required for a detection to be considered valid. With improved prompts, you can set a higher threshold. Here's how to do it:

  • CONF_TH = 0.25 - Start here with your new game-specific prompts. This is a good starting point for testing.
  • CONF_TH = 0.35 - Aim for this value in production use. This will reduce false positives while ensuring good detection rates.

Adjusting your confidence threshold should follow as you make other changes, so that the system is best tuned to your needs. This is why it is so important to iterate.

Expected Improvements: What You Can Expect

By implementing these game-specific optimization strategies, you should see some significant improvements in your game detection. Here's what you can expect:

  • Detection Accuracy: You could see improvements ranging from 50% to 200% or more. The difference will depend on how generic your starting prompts were and how well you tailor them. This is a huge increase.
  • False Positives: You should experience a significant reduction in false positives. This means the model will misidentify objects less frequently, which will increase overall accuracy.
  • Confidence Scores: Instead of seeing confidence scores in the 0.1-0.3 range, you should see scores between 0.4 and 0.8 or higher. This will let you know the model is very confident in its detections.

These improvements will lead to a much more reliable and enjoyable gaming experience.

Implementation Plan: Putting It All Together

Ready to put these strategies into action? Here's a step-by-step implementation plan:

  1. Document Your Game: Start by documenting your game's visual elements. Take screenshots, list enemy types, and describe the overall visual style. This will be your foundation for prompt creation.
  2. Create a Prompt Library: Develop a library of 10-15 specific prompts. Make sure these prompts are detailed and tailored to your game's unique characteristics.
  3. A/B Test Prompts: Compare the performance of your new, specific prompts against your old, generic prompts. See how each performs in terms of accuracy and confidence scores.
  4. Optimize Threshold: Once you have the new prompts working, find the best CONF_TH value to minimize false positives while still maintaining good detection rates.
  5. Iterate and Refine: Based on gameplay testing, continue to refine your prompts and adjust your confidence threshold. You can also adjust the environment to optimize detection.

By following this plan, you'll be well on your way to boosting your game detection confidence.

Files to Modify

Here are the specific files you'll need to modify to implement these changes:

  • local_client_yoloe_ws.py: You'll update the PROMPTS and CONF_TH variables in this file.
  • remote_server_yoloe_ws.py: You will update the DEFAULT_PROMPTS variable in this file.

Make sure to back up your files before making any changes and test your implementation in a controlled environment.

This is a critical step in creating reliable game detection systems. By following the process, you'll see a marked improvement in detection accuracy and the overall performance of your game.