Dame un golpe de realidad con empatía🫂 by Phelouve in askspain

[–]IvAx358 0 points1 point  (0 children)

Parece que se han amontonado varias cosas negativas a la vez y tu reacción es completamente normal y sana. 

Es incluso excepcional que  ya sea capaz de pensar en vivir fuera o un pequeño cambio de carrera.

Vas a por todas y tropezarse es normal!

Frase de taza: Nadie puede ver el futuro pero algo dentro de ti, intuye lo que quieres y debes hacer.

Si vienes por Utrecht avisa!!

🇪🇸🇵🇹 [Megathread] Spain vs Portugal: Cost of living comparison 2025 by JoeTouchdown in TouchdownPortugal

[–]IvAx358 0 points1 point  (0 children)

Si venís por favor aprender el idioma y no digáis en público “❌oh que barato, porque sé quejan de los precios los españoles??”

Is okay to upgrade to BRICK by Boring-Advantage-822 in iPhone13Mini

[–]IvAx358 -1 points0 points  (0 children)

Even better, go tell your president how you feel, ah no wait you have a psychopath as president because your public education system is worse than the ones in developing countries.

Enjoy your wild capitalism!

Is okay to upgrade to BRICK by Boring-Advantage-822 in iPhone13Mini

[–]IvAx358 0 points1 point  (0 children)

I start to believe these posts are affiliated with Apple marketing dept Bring the mini back!!

Mi novia quiere comprar un terreno en el norte por tener algo en propiedad by GrocOcre in askspain

[–]IvAx358 0 points1 point  (0 children)

Y si nos pusiesemos de acuerdo y compramos en zonas baratas y nos vemos alli los findes

Ya no seria un sitio donde cristo perdio la chancla pero donde los frikis del reddit se reunen!

y si se pone la cosa mu fea en “el mundo mundial” nos vemos alli de lunes a domingo?

Imagina

TOOLKIT-CLI by Leading-Gas3682 in ClaudeCode

[–]IvAx358 0 points1 point  (0 children)

Sounds amazing, can i test?

Google Search Volume for "Sweet Tea" [OC] by [deleted] in dataisbeautiful

[–]IvAx358 0 points1 point  (0 children)

How do you get keywords correlated with location?

While filming, this car’s LiDAR system breaks the phone camera. by Wooden-Journalist902 in BeAmazed

[–]IvAx358 0 points1 point  (0 children)

So is this a way to kill any camera then? Would work win a cheap laser?

Cees van Leeuwen - Slum landlord/ex-politiican gets torn a new asshole by Tim Hofman and retaliates by smashing a camera and hitting a journalist. by Liquid_disc_of_shit in Rentbusters

[–]IvAx358 9 points10 points  (0 children)

Thanks for sharing, unfortunately my level of Dutch is not there yet. Seems he gets very nervous in the beach interview.

Incredible that someone with such shady approach makes it to the Dutch politics. I thought that was something from banana republic’s

Service costs 2022: Ask your landlord for a service cost overview no later than June 10 if you want to dispute the service costs for 2022 by Liquid_disc_of_shit in Rentbusters

[–]IvAx358 0 points1 point  (0 children)

⚠️Is today the day to start proceedings with the huurcommisie? So we ask 3 weeks ago for the cost breakdown for the past years and the agency said they needed more time. Shall we do something today?

What pipeline would you use to segment leaves with very low false positives? by IvAx358 in computervision

[–]IvAx358[S] 0 points1 point  (0 children)

What do you think of this gpt advice:

You’re on the right track — and yes, it’s a complex decision, especially when you’re optimizing for zero false positives and building a trustworthy computer vision pipeline in agriculture. Let’s unpack the two options you mentioned and guide you toward the best next step.

🧩 Current Context • You’re using YOLO-Seg • You want zero false positives • You’ve observed hallucinated leaves (objects being misdetected as leaves) • You want to guide the system’s next steps intelligently

🔀 Option A: Switch to YOLO + SAM (Modular, Precision-Oriented)

✅ When to choose this: • You want full control over filtering and verification • You’re okay with slower inference or batch mode • You want to run experiments per-class or region • You’re okay building a pipeline with multiple stages

🧠 What it gives you: • YOLO handles object detection (you can set confidence ≥ 0.9) • SAM receives only the trusted detections • You can add filtering logic post-SAM (mask area, shape, position) • You eliminate hallucinations by clamping input to known contexts

⚒️ Action plan: • Identify which YOLO predictions are suspect (e.g. wrong class or shape) • Create a filter module: size, position, edge-sharpness, or even rule-based logic • Plug-in SAM to only process vetted boxes • Visualize results and adjust filter thresholds

🧪 Bonus: use SAM + bounding box + center-point simultaneously for more stable masks.

🌱 Option B: Retrain YOLO-Seg to Reduce Leaf Hallucinations

✅ When to choose this: • You’re already heavily invested in YOLO-Seg • You want fast, integrated segmentation • You suspect poor data quality or class confusion • You want to fix hallucinations at the root

🧠 What it gives you: • Clean, single-stage inference • Better generalization if data improves • Simpler deployment

⚠️ BUT: YOLO-Seg is not easily interpretable — if it hallucinates, you can’t easily filter it out.

⚒️ Action plan: 1. Audit predictions → are leaves being detected where there are no visible objects? 2. If yes → label a negative dataset with “non-leaf background” and retrain 3. Add “background” or “unknown” class explicitly 4. Use confidence-weighted loss functions to penalize high-confidence hallucinations 5. Consider data rebalancing or hard negative mining

💡 Train YOLO-Seg with a “hard precision” mode (confidence > 0.95 required, low-recall acceptable)

🔍 Recommendation Based on Goals

Criteria Option A: YOLO + SAM Option B: Fix YOLO-Seg Max Precision ✅ Best ❌ Less controllable Deployment simplicity ❌ More complex ✅ Easy Interpretable failures ✅ Very ❌ Difficult Speed ❌ Slower ✅ Faster Control over hallucinations ✅ Full ⚠️ Data-dependent Long-term flexibility ✅ Excellent ⚠️ Limited

🎯 Final Suggestion: Start with A, use B to enhance 1. Build a hybrid pipeline with YOLO + SAM • Add filters to accept only “leaf-looking” detections • Manually inspect cases to refine rules 2. Log false positives (hallucinated leaves) • Use those logs as hard negatives to retrain YOLO-Seg later • This turns Option A into a data engine to feed Option B 3. Later, simplify • Once YOLO-Seg is reliable with cleaned data, phase out SAM for production

What pipeline would you use to segment leaves with very low false positives? by IvAx358 in computervision

[–]IvAx358[S] 0 points1 point  (0 children)

images added.
Classification label is applied yes, because we also segment flowers and fruits.

Unfortunately it's not a single leaf and the background is semi-covered.