Lobe
機械学習 · 636.1M 月間訪問数
Lobeは、MacおよびWindows向けの無料の使いやすいデスクトップアプリケーションで、コードを一切書かずにカスタムの機械学習モデルを構築、トレーニング、デプロイできます。主に画像分類に焦点を当て、AI作成のプロセスを簡素化します。
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TensorFlow
フレームワーク · 688.6K 月間訪問数
TensorFlowは、Googleが開発したエンドツーエンドのオープンソース機械学習プラットフォームです。研究者や開発者がMLを活用したアプリケーションを構築・展開できるよう、ツール、ライブラリ、コミュニティリソースからなる包括的で柔軟なエコシステムを提供します。初心者から専門家まで、TensorFlowは簡単なモデル構築のための直感的な高レベルAPIと、高度な研究のための強力な低レベルAPIを提供し、サーバー、エッジデバイス、ブラウザへの展開を可能にします。
製品概要
Lobe 製品概要
Lobeは、MacおよびWindows向けの無料の使いやすいデスクトップアプリケーションで、コードを一切書かずにカスタムの機械学習モデルを構築、トレーニング、デプロイできます。主に画像分類に焦点を当て、AI作成のプロセスを簡素化します。
Preview
TensorFlow 製品概要
TensorFlowは、Googleが開発したエンドツーエンドのオープンソース機械学習プラットフォームです。研究者や開発者がMLを活用したアプリケーションを構築・展開できるよう、ツール、ライブラリ、コミュニティリソースからなる包括的で柔軟なエコシステムを提供します。初心者から専門家まで、TensorFlowは簡単なモデル構築のための直感的な高レベルAPIと、高度な研究のための強力な低レベルAPIを提供し、サーバー、エッジデバイス、ブラウザへの展開を可能にします。
Preview
Detailed feature comparison
| Feature | Lobe | TensorFlow |
|---|---|---|
| 主要カテゴリー | 機械学習 | フレームワーク |
| 追加日 | 2025-08-01 | 2025-08-11 |
| 価格 | 無料 | 無料 |
| 公式サイト | github.com | www.tensorflow.org |
| 製品タイプ | アプリ | ウェブサイト |
| Performance data | ||
| ユーザー評価 | 未確認 | 未確認 |
| コメント | 0 | 0 |
| 月間訪問数 | 636.1M | 688.6K |
| 月間成長率 | 0.8% | -6.3% |
| お気に入り | 115 | 74 |
| Details | 詳細を見る | 詳細を見る |
Lobe vs TensorFlow monthly traffic
Compare Lobe and TensorFlow by monthly reach, traffic trend, visit depth, top regions, and acquisition sources.
How to interpret the traffic data
In the Lobe vs TensorFlow monthly traffic comparison, Lobe currently shows 636.1M visits and TensorFlow shows 688.6K; Lobe has about 923.7 times the visible traffic of TensorFlow, an absolute difference of about 635.4M visits. This reflects visible reach, not feature quality or paid users.
Both tools provide verified traffic details, so monthly trends, visit depth, regions, and acquisition sources can be compared on the same basis.
Lobe is registered at the github.com/lobe subpage. Because the current data does not state whether other host pages are excluded, treat this as domain-level context rather than independent traffic for one product or project.
Lobe monthly traffic:
Latest traffic
月間訪問数
636.1M
平均滞在時間
6:23
訪問あたりページ数
5.92
直帰率
36.46%
Data updated 2026-06-15
Monthly traffic trend
- 2026/1: 542.6M 月間訪問数
- 2026/2: 534.8M 月間訪問数
- 2026/3: 634.3M 月間訪問数
- 2026/4: 631M 月間訪問数
- 2026/5: 636.1M 月間訪問数
主要地域
Top 5 countries/regions
| Country/region | Percentage | Traffic |
|---|---|---|
| 🇺🇸United States | 36.14% | 229.9M |
| 🇨🇳China | 22.96% | 146M |
| 🇮🇳India | 17.41% | 110.7M |
| 🇷🇺Russia | 15.84% | 100.8M |
| 🇩🇪Germany | 7.65% | 48.7M |
流入元
| Source type | Percentage | Traffic |
|---|---|---|
| ダイレクト | 82.14% | 522.5M |
| 参照元 | 16.14% | 102.7M |
| Eメール | 1.72% | 10.9M |
検索キーワード
githubgithub copilothermes agentzapretзапрет
TensorFlow monthly traffic:
Latest traffic
月間訪問数
688.6K
平均滞在時間
1:55
訪問あたりページ数
7.28
直帰率
50.17%
Data updated 2026-06-15
Monthly traffic trend
- 2025/9: 894.8K 月間訪問数
- 2026/1: 811K 月間訪問数
- 2026/2: 769.2K 月間訪問数
- 2026/3: 803.4K 月間訪問数
- 2026/4: 735.1K 月間訪問数
- 2026/5: 688.6K 月間訪問数
主要地域
Top 5 countries/regions
| Country/region | Percentage | Traffic |
|---|---|---|
| 🇺🇸United States | 40.89% | 281.6K |
| 🇮🇳India | 36.17% | 249.1K |
| 🇩🇪Germany | 9.26% | 63.8K |
| 🇳🇬Nigeria | 6.94% | 47.8K |
| 🇨🇳China | 6.74% | 46.4K |
流入元
| Source type | Percentage | Traffic |
|---|---|---|
| ダイレクト | 63.62% | 438.1K |
| 参照元 | 33.53% | 230.9K |
| Eメール | 2.85% | 19.6K |
検索キーワード
tensorboardtensor flowtensorflowtensorflow playgroundword2vec
Traffic-based selection guidance: Lobe is registered under a github.com subpath, so its large visible total may include the host platform. The current data does not justify choosing Lobe for traffic alone; shortlist by unique taxonomy and use case, then validate with the same tasks.
Usage comparison
Compare the core capabilities of Lobe and TensorFlow
Lobe Core features
機械学習
STEM
ノーコード
TensorFlow Core features
機械学習
フレームワーク
開発者ツール
Use cases
Lobe Use cases
コンピュータビジョン
機械学習
モデル学習
デスクトップアプリ
開発者ツール
無料
画像分類
マイクロソフト
ノーコード
プロトタイピング
TensorFlow Use cases
コンピュータビジョン
機械学習
モデル学習
データサイエンス
ディープラーニング
デプロイ
グーグル
ニューラルネットワーク
NLP
オープンソース
Python
Lobe vs TensorFlow：In-depth comparison and selection guidance
First decide whether the products solve the same kind of need
This in-depth Lobe vs TensorFlow comparison uses only the product records, taxonomy, audience, traffic, and community signals available on this page. Lobe is primarily listed under “機械学習”, while TensorFlow is primarily listed under “フレームワーク”, so the first decision is whether your actual task matches their recorded scope.
The structured fields currently show these decision-relevant differences: Primary category (Lobe: 機械学習; TensorFlow: フレームワーク); Product type (Lobe: App; TensorFlow: Website); Monthly visits (Lobe: 636.1M; TensorFlow: 688.6K); Monthly growth (Lobe: 0.8%; TensorFlow: -6.3%); Favorites (Lobe: 115; TensorFlow: 74). These facts are more useful for selection than brand visibility alone.
What market visibility and monthly traffic mean
In the Lobe vs TensorFlow monthly traffic comparison, Lobe currently shows 636.1M visits and TensorFlow shows 688.6K; Lobe has about 923.7 times the visible traffic of TensorFlow, an absolute difference of about 635.4M visits. This reflects visible reach, not feature quality or paid users.
Both tools provide verified traffic details, so monthly trends, visit depth, regions, and acquisition sources can be compared on the same basis.
Lobe is registered at the github.com/lobe subpage. Because the current data does not state whether other host pages are excluded, treat this as domain-level context rather than independent traffic for one product or project.
Lobe is registered under a github.com subpath, so its large visible total may include the host platform. The current data does not justify choosing Lobe for traffic alone; shortlist by unique taxonomy and use case, then validate with the same tasks.
Product positioning, use cases, and roles
Lobe and TensorFlow currently overlap in shared categories: 機械学習; shared tags: コンピュータビジョン、機械学習、モデル学習. This can place both on the same shortlist, but it does not prove equal implementation, depth, or cost.
Lobe's unique categories/tags are STEM、ノーコード、デスクトップアプリ、開発者ツール、無料、画像分類、マイクロソフト、プロトタイピング; TensorFlow's are フレームワーク、開発者ツール、データサイエンス、ディープラーニング、デプロイ、グーグル、ニューラルネットワーク、NLP. These unique fields are the strongest differentiators: validate the product whose recorded scope matches the task instead of following traffic alone.
What ratings, comments, and favorites can tell you
Lobe has no verified rating, 0 comments, 115 favorites, and 110 likes；TensorFlow has no verified rating, 0 comments, 74 favorites, and 68 likes。
Neither product has enough rating or comment samples for a credible reputation ranking.
Selection guidance by actual need
When to evaluate Lobe first
Put Lobe on the priority trial list when the task aligns with “機械学習” and especially STEM、ノーコード、デスクトップアプリ、開発者ツール、無料、画像分類. This follows recorded positioning and does not imply unlisted capabilities are absent.
Lobe also currently records: pricing is free, product type is app, 636.1M monthly visits shown for the registered host (subpage scope unknown), no verified user rating. Verify any hard requirement around price, platform, or reach before trial, and do not let sparse review data substitute for testing.
When to evaluate TensorFlow first
Put TensorFlow on the priority trial list when the task aligns with “フレームワーク” and especially フレームワーク、開発者ツール、データサイエンス、ディープラーニング、デプロイ、グーグル. This follows recorded positioning and does not imply unlisted capabilities are absent.
TensorFlow also currently records: pricing is free, product type is website, 688.6K verified monthly visits, no verified user rating. Verify any hard requirement around price, platform, or reach before trial, and do not let sparse review data substitute for testing.
How to validate the recommendation before deciding
The available data describes positioning, public visibility, and community signals, but it cannot prove output quality, speed, integration effort, privacy, or long-term cost in your workflow. Before deciding, run the same representative tasks in Lobe and TensorFlow, then record completion time, accuracy, manual corrections, and the real paid threshold. A like-for-like trial turns this comparison into a defensible adoption decision.
比較 FAQ
How should I choose between Lobe and TensorFlow?
Compare positioning, pricing, taxonomy, and traffic maturity, then verify the latest details on each official website.
Where does this comparison data come from?
The factual baseline is derived from product, taxonomy, traffic, and community data. Reviewed editorial conclusions show their source and verification date.
What do unknown fields mean?
Unknown means there is not enough reliable evidence; the page does not fill gaps with assumptions.