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Source channel @githubtrending · Post #14627 · Apr 24
#jupyter_notebook DINOv2 is a powerful AI model from Meta AI that learns to understand images without needing labeled data, using self-supervised learning. It was trained on 142 million images and creates strong visual features that work well for many tasks like image classification, depth estimation, and segmentation without extra fine-tuning. You can use its pretrained models easily with simple classifiers, saving time and effort. DINOv2 is efficient, scalable, and performs better than many other models, making it great for building versatile computer vision applications quickly and accurately. It’s open-source and ready to use with PyTorch. https://github.com/facebookresearch/dinov2
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@ethereumglobalnews · Post #1454 · 12/01/2025, 06:57 AM
🪙 Vitalik: “You can just build on #L1” as fees stay cheap in 2025. #ETH 😎 Vitalik 表示: 由於 2025 年以太坊交易費持續保持低位，「直接在 L1 上構建」依然可行。今年以來 L1 需求增速溫和、區塊空間壓力未現顯著擁堵。 #Ethereum#DeFi#以太坊#市場趨勢 ——— ⚡️ 若費用長期維持低檔，L1 與 Rollup 的功能分工可能再度被市場重估 #Scaling ✅Chat: @Web3NewsInsight 🦂 👇Tip👇讚 或點擊進行💎資源搜索👇
@ethereumglobalnews · Post #1618 · 12/26/2025, 04:57 AM
🪙 L1 Tokens 2025 Performance Castle Labs data shows most Layer 1 tokens ended 2025 in negative territory. Only BNB (+18.2%) and TRX (+9.8%) managed to stay in positive returns. • ETH：-15.3% • SOL：-35.9% • SUI / AVAX：跌幅均超 -67% • TON：全年回撤接近 -74% ⚡️ 結構性行情下L1 不再齊漲齊跌 #Ethereum#L1#CryptoMarkets #OnChain#BNB#以太坊 —————— 👇⭐️👇 🤣 🥲👇 資源搜索 🖲️👆
@deep_school · Post #83 · 09/20/2022, 02:35 PM
Сегодня вторник, а значит в эфире рубрика “повторяем теорию”🤓 Вспомним про регуляризацию сетей, а именно про три популярных метода: L1, L2 и Dropout (ведь был популярен когда-то, надо отдать дань старичку). Статья в телеграфе 👉Регуляризуем правильно! #регуляризация#L1#L2#dropout
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