モデル情報

textnet-base

czczup · textnet-base

TextNet is a lightweight and efficient architecture designed specifically for text detection, offering superior performance compared to traditional models like MobileNetV3. With variants **TextNet-T**, **TextNet-S**, and **TextNet-B** (6.8M, 8.0M, and 8.9M parameters respectively), it achieves an excellent balance between accuracy and inference speed. TextNet achieves state-of-the-art results in text detection, outperforming hand-crafted models in both accuracy and speed. Its architecture is highly efficient, making it ideal for GPU-based applications. We first compare TextNet with representative hand-crafted backbones, such as ResNets and VGG16. For a fair comparison, all models are first pre-trained on IC17-MLT [52] and then finetuned on Total-Text. The proposed TextNet models achieve a better trade-off between accuracy and inference speed than previous hand-crafted models by a significant margin. In addition, notably, our TextNet-T, -S, and -B only have 6.8M, 8.0M, and 8.9M parameters respectively, which are more parameter-efficient than ResNets and VGG16. These results demonstrate that TextNet models are effective for text detection on the GPU device.

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入力 → 出力待核验 → 待核验
モデルウェイト未確認
情報更新日時2026/11/2 19:17:04
OVERVIEW

モデル紹介

TextNet is a lightweight and efficient architecture designed specifically for text detection, offering superior performance compared to traditional models like MobileNetV3. With variants **TextNet-T**, **TextNet-S**, and **TextNet-B** (6.8M, 8.0M, and 8.9M parameters respectively), it achieves an excellent balance between accuracy and inference speed. TextNet achieves state-of-the-art results in text detection, outperforming hand-crafted models in both accuracy and speed. Its architecture is highly efficient, making it ideal for GPU-based applications. We first compare TextNet with representative hand-crafted backbones, such as ResNets and VGG16. For a fair comparison, all models are first pre-trained on IC17-MLT [52] and then finetuned on Total-Text. The proposed TextNet models achieve a better trade-off between accuracy and inference speed than previous hand-crafted models by a significant margin. In addition, notably, our TextNet-T, -S, and -B only have 6.8M, 8.0M, and 8.9M parameters respectively, which are more parameter-efficient than ResNets and VGG16. These results demonstrate that TextNet models are effective for text detection on the GPU device.

主な特徴

特点摘要待依据来源补齐,不凭模型名称推断能力。

制約と注意点

具体的な制約を判断する出典が不足しています。実際の性能、遅延、データ方針、ライセンス、提供期間を確認してください。

PRICING & COST

出典別料金と費用分析

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出典・チャネル入力出力課金単位収集日時
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月額の基本トークン費用を試算

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INDEPENDENT BENCHMARKS

公開評価と実行設定

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

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よくある質問

オープンウェイトなら無料で商用利用できますか?

必ずしもそうではありません。バージョン別のライセンス、商用条件、利用方針を確認してください。自前運用には計算資源と保守費用も必要です。

これはリアルタイムの請求料金ですか?

収集日時付きの料金スナップショットです。最新のバージョン、地域、割引、課金方式は各チャネルで確認してください。

出典と追跡記録

许可证:来源未披露,须进一步核验。模型介绍与第三方评测均以原来源的具体版本和方法为准。