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Common Pitfalls in Computer Vision & AI Projects (and How to Avoid Them)

Artificial Intelligence : Papers & Concepts

Release Date: 10/01/2025

RF-DETR: Neural Architecture Search for Real-Time Detection Transformers show art RF-DETR: Neural Architecture Search for Real-Time Detection Transformers

Artificial Intelligence : Papers & Concepts

In this episode of Artificial Intelligence: Papers and Concepts, we break down RF-DETR, a new direction in object detection that challenges the idea of fixed-capacity models. Instead of choosing between speed and accuracy upfront, RF-DETR introduces an elastic detector that adapts its computation dynamically at inference time. We explore how RF-DETR reuses intermediate representations to scale up or down on demand, why this matters for real-world deployment on edge and cloud systems, and how this design enables more predictable performance across diverse hardware constraints. If you’re...

info_outline YOLO26: Rethinking Real-Time Vision for the Edge show art YOLO26: Rethinking Real-Time Vision for the Edge

Artificial Intelligence : Papers & Concepts

In this episode of Artificial Intelligence: Papers and Concepts, we break down YOLO26, a major shift in real-time object detection. Instead of chasing raw accuracy, YOLO26 is designed for speed, consistency, and edge deployment. We explore how removing non-maximum suppression (NMS) delivers predictable low-latency inference, why simplifying the loss functions makes the model easier to deploy on real hardware, and how new training ideas borrowed from large language models improve small-object detection. If you’re building vision systems for robots, drones, factories, or mobile devices,...

info_outline DeepSeek mHC show art DeepSeek mHC

Artificial Intelligence : Papers & Concepts

Why do some large AI models suddenly collapse during training—and how can geometry prevent it? In this episode of Artificial Intelligence: Papers and Concepts, we break down DeepSeek AI’s Manifold-Constrained Hyperconnections (mHC), a new architectural approach that fixes training instability in large language models. We explore why traditional hyperconnections caused catastrophic signal explosions, and how constraining them to a geometric structure—doubly stochastic matrices on the Birkhoff polytope—restores stability at scale. You’ll learn how mHC reduces signal amplification from...

info_outline Chinchilla Scaling Law show art Chinchilla Scaling Law

Artificial Intelligence : Papers & Concepts

In this episode of Artificial Intelligence: Papers and Concepts, curated by Dr. Satya Mallick, we break down DeepMind’s 2022 paper “Training Compute-Optimal Large Language Models”—the work that challenged the “bigger is always better” era of LLM scaling. You’ll learn why many famous models were under-trained, what it means to be compute-optimal, and why the best performance comes from scaling model size and training data together. We also unpack the Chinchilla vs. Gopher showdown, why Chinchilla won with the same compute budget, and what this shift means for the future:...

info_outline Gradient-Based Planning show art Gradient-Based Planning

Artificial Intelligence : Papers & Concepts

How should an AI or robot decide what to do next? In this episode, we explore a new approach to planning that rethinks how world models are trained. The episode is based on the paper "Closing the Train-Test Gap in World Models for Gradient-Based Planning" Many AI systems can predict the future accurately, yet struggle when asked to plan actions efficiently. We explain why this train–test mismatch hurts performance and how gradient-based planning offers a faster alternative to traditional trial-and-error or heavy optimization. The key idea is simple but powerful: if you want a model to plan...

info_outline SAM3D: The Next Leap in 3D Understanding show art SAM3D: The Next Leap in 3D Understanding

Artificial Intelligence : Papers & Concepts

Forget flat photos—SAM3D is rewriting how machines understand the world. In this episode, we break down the groundbreaking new model that takes the core ideas of Meta’s Segment Anything Model and expands them into the third dimension, enabling instant 3D segmentation from just a single image. We start with the limitations of traditional 2D vision systems and explain why 3D understanding has always been one of the hardest problems in computer vision. Then we unpack the SAM3D architecture in simple terms: its depth-aware encoder, its multi-plane representation, and how it learns to infer 3D...

info_outline DINOv3 : A new Self-Supervised Learning (SSL) Vision Language Model (VLM) show art DINOv3 : A new Self-Supervised Learning (SSL) Vision Language Model (VLM)

Artificial Intelligence : Papers & Concepts

In this episode, we explore DINOv3, a new self-supervised learning (SSL) vision foundation model from Meta AI Research, emphasizing its ability to scale effortlessly to massive datasets and large architectures without relying on manual data annotation. The core innovations are scaling model and dataset size, introducing Gram anchoring to prevent the degradation of dense feature maps during long training, and employing post-hoc strategies for enhanced flexibility in resolution and text alignment. The authors present DINOv3 as a versatile visual encoder that achieves...

info_outline dots.ocr SOTA Document Parsing in a Compact VLM show art dots.ocr SOTA Document Parsing in a Compact VLM

Artificial Intelligence : Papers & Concepts

dots.ocr is a powerful, multilingual document parsing model from rednote-hilab that achieves state-of-the-art performance by unifying layout detection and content recognition within a single, efficient vision-language model (VLM). Built upon a compact 1.7B parameter Large Language Model (LLM), it offers a streamlined alternative to complex, multi-model pipelines, enabling faster inference speeds. The model demonstrates superior capabilities across multiple industry benchmarks, including OmniDocBench, where it leads in text, table, and reading order tasks, and olmOCR-bench, where...

info_outline DeepSeek-OCR : A Revolutionary Idea show art DeepSeek-OCR : A Revolutionary Idea

Artificial Intelligence : Papers & Concepts

In this episode, we dive deep into DeepSeek-OCR, a cutting-edge open-source Optical Character Recognition (OCR) / Text Recognition model that’s redefining accuracy and efficiency in document understanding. DeepSeek-OCR flips long-context processing on its head by rendering text as images and then decoding it back—shrinking context length by 7–20× while preserving high fidelity. We break down how the two-stage stack works—DeepEncoder (optical/vision encoding of pages) + MoE decoder (text reconstruction and reasoning)—and why this “context optical compression” matters for...

info_outline nanochat by Karpathy - How to build your own ChatGPT for $100 show art nanochat by Karpathy - How to build your own ChatGPT for $100

Artificial Intelligence : Papers & Concepts

“The best ChatGPT that $100 can buy.” That’s Andrej Karpathy’s positioning for nanochat—a compact, end‑to‑end stack that goes from tokenizer training to a ChatGPT‑style web UI in a few thousand lines of Python (plus a tiny Rust tokenizer). It’s meant to be read, hacked, and run so students, researchers, and tech enthusiats can understand the entire pipeline needed to train a baby version of ChatGPT.  In this episode, we walk you through the nanochat repository.  Resources nanochat github repo: AI Consulting & Product Development Services: ...

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In this episode, we dig deep into the unglamorous side of AI and computer vision projects — the mistakes, misfires, and blind spots that too often derail even the most promising teams. Based on BigVision.ai’s playbook “Common Pitfalls in Computer Vision & AI Projects”, we walk through a field-tested catalog of pitfalls drawn from real failures and successes.

We cover:

  • Why ambiguous problem statements and fuzzy success criteria lead to early project drift

  • The dangers of unrepresentative training data and how missing edge cases sabotage models

  • Labeling mistakes, data leakage, and splits that inflate your offline metrics

  • The trap of being model-centric instead of data-centric

  • Shortcut learning, spurious correlations, and how models “cheat”

  • Misaligned metrics, thresholds, and how optimizing the wrong thing kills business impact

  • Over-engineering vs. solid baselines

  • The ambition vs. reproducibility tension (drift, code, data versioning)

  • Deployment constraints, monitoring, silent failures, and how AI degrades in the wild

  • Fairness, safety, adversarial robustness, and societal risks

  • Human factors, UX, privacy, compliance, and integrating AI into real workflows

  • ROI illusions: why model accuracy alone doesn’t pay the bills

We also reveal their “pre-flight checklist” — a lean but powerful go/no-go tool to ensure your project is grounded in real needs and avoids death by scope creep.

Why listen?
This isn’t theory — it’s a survival guide. Whether you’re a founder, ML engineer, product lead, or AI skeptic, you’ll pick up concrete lessons you can apply before you spend millions. Avoiding these traps could be the difference between shipping a brittle proof-of-concept and deploying a real, reliable system that delivers value.

Tune in for cautionary tales, war stories, and actionable tactics you can steal for your next vision project.

Resources

  1. https://bigvision.ai/pitfalls  [PDF]
  2. Big Vision LLC - Computer Vision and AI Consulting Services.
  3. OpenCV University - Start your AI Career today!