ConvNeXt

Headlines · 2

1. 11h
   Arrow detection with a ConvNeXt backbone pretrained on DINOv3

   A Reddit user, BrashBunyip, posted an archery arrow-detection project on r/MachineLearning, building a dataset from roughly 40,000 rendered Blender scenes plus 8,000 hand-labelled real images, augmented into about 1 million training samples for separate registration and detection networks. The author reports that ImageNet-pretrained ResNets performed quite well and a pure ViT worse, while ConvNeXt pretrained on DINOv3 performed best. Self-reported test results were F1 96.6% and 80.5% whole-image correctness, with no missed arrows and no false detections. The pipeline registers the target to find rings, centre and scoring zones, produces an impact heatmap from the cropped image, runs the detector mirrored, applies a homography to remove perspective skew and adds a radial correction for warped paper.

   r/MachineLearning · 🔥 4
2. 4d
   Ex-OpenAI researcher's essay argues neural networks are far less tangled than assumed

   Nick Cammarata, now a researcher at the interpretability lab Goodfire, published a long essay titled "Neural Networks Are Already Clean" that rejects the pessimistic view he helped shape years ago: with better tools and AI agents, he argues, most neurons in large models can be given fairly clear functional explanations. He estimates that in Qwen3-32B about 90 percent of neuron poles have one main job, about 7 percent have two related ones and only 2–4 percent remain unexplained. He revisits the classic InceptionV1 "cat face/car front" neuron, arguing it actually detects V-shaped geometry, and proposes "recursive generalization," in which a neuron responding to "iy" extends to "diy" and then to "do it yourself." The essay has not been peer-reviewed or independently replicated, and a technical companion paper has not been released; OpenAI's Roon called it a historic moment, while Google DeepMind's Neel Nanda warned against overhyping it.

   MIT科技评论中文 · 🔥 0

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