> For the complete documentation index, see [llms.txt](https://docs.roboflow.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.roboflow.com/roboflow/roboflow-jp/deploy/supported-models/roboflow-3.md).

# Roboflow 3.0

Roboflow 3.0 は Roboflow 独自のモデルアーキテクチャです。物体検出、インスタンスセグメンテーション、分類、キーポイント検出をサポートしています。Roboflow 3.0 モデルは Roboflow プラットフォームで学習し、当社の [Serverless Hosted API](/roboflow/roboflow-jp/deploy/serverless-hosted-api-v2.md).

セルフホスト型デプロイについては、以下を参照してください。 [Roboflow Inference](https://inference.roboflow.com/).

{% hint style="info" %}
Roboflow 3.0 には公開されたデフォルトの COCO エイリアスはありません。独自に学習したモデルは、その `project/version` の識別子を使用して呼び出します。あなたの [Roboflow Project](https://app.roboflow.com/).
{% endhint %}

## コードサンプル

次をインストールしてください： [Inference SDK](https://inference.roboflow.com/) と [supervision](https://supervision.roboflow.com/):

```bash
pip install inference-sdk supervision
```

以下のサンプルでは、学習済みの Roboflow 3.0 モデルに対して推論を実行します。次のものを置き換えてください。 `your-project/1` を、モデルの URL とバージョンに置き換えてください。あなたの [Roboflow API Key](https://app.roboflow.com/settings/api) を `API_KEY` 環境変数に設定してください。

### 物体検出

```python
import os
import cv2
import urllib.request
import supervision as sv
from inference_sdk import InferenceHTTPClient

image_url = "https://storage.googleapis.com/com-roboflow-marketing/notebooks/examples/cars-highway.png"
image_path = "cars-highway.png"
urllib.request.urlretrieve(image_url, image_path)

image = cv2.imread(image_path)

client = InferenceHTTPClient(
    api_url="https://serverless.roboflow.com",
    api_key=os.getenv("API_KEY"),
)
result = client.infer(image, model_id="your-project/1")

detections = sv.Detections.from_inference(result)
labels = [
    f"{class_name} {confidence:.2f}"
    for class_name, confidence
    in zip(detections.data.get("class_name", []), detections.confidence)
]

annotated = sv.BoxAnnotator().annotate(scene=image.copy(), detections=detections)
annotated = sv.LabelAnnotator().annotate(scene=annotated, detections=detections, labels=labels)
cv2.imwrite("output.png", annotated)
```

### インスタンスセグメンテーション

```python
import os
import cv2
import urllib.request
import supervision as sv
from inference_sdk import InferenceHTTPClient

image_url = "https://storage.googleapis.com/com-roboflow-marketing/notebooks/examples/bicycle.png"
image_path = "bicycle.png"
urllib.request.urlretrieve(image_url, image_path)

image = cv2.imread(image_path)

client = InferenceHTTPClient(
    api_url="https://serverless.roboflow.com",
    api_key=os.getenv("API_KEY"),
)
result = client.infer(image, model_id="your-project/1")

detections = sv.Detections.from_inference(result)
labels = [
    f"{class_name} {confidence:.2f}"
    for class_name, confidence
    in zip(detections.data.get("class_name", []), detections.confidence)
]

annotated = sv.MaskAnnotator().annotate(scene=image.copy(), detections=detections)
annotated = sv.LabelAnnotator().annotate(scene=annotated, detections=detections, labels=labels)
cv2.imwrite("output.png", annotated)
```

### キーポイント検出

```python
import os
import cv2
import urllib.request
import supervision as sv
from inference_sdk import InferenceHTTPClient

image_url = "https://storage.googleapis.com/com-roboflow-marketing/notebooks/examples/person-walking.png"
image_path = "person-walking.png"
urllib.request.urlretrieve(image_url, image_path)

image = cv2.imread(image_path)

client = InferenceHTTPClient(
    api_url="https://serverless.roboflow.com",
    api_key=os.getenv("API_KEY"),
)
result = client.infer(image, model_id="your-project/1")

key_points = sv.KeyPoints.from_inference(result)

annotated = sv.EdgeAnnotator(color=sv.Color.BLUE, thickness=2).annotate(
    scene=image.copy(), key_points=key_points
)
annotated = sv.VertexAnnotator(color=sv.Color.GREEN, radius=5).annotate(
    scene=annotated, key_points=key_points
)
cv2.imwrite("output.png", annotated)
```

### 分類

Classification の応答には、信頼度付きのクラス予測のリストが含まれるため、可視化は適用できません。応答からトップクラスを直接読み取ってください。

```python
import os
import cv2
import urllib.request
from inference_sdk import InferenceHTTPClient

image_url = "https://storage.googleapis.com/com-roboflow-marketing/notebooks/examples/cars-highway.png"
image_path = "cars-highway.png"
urllib.request.urlretrieve(image_url, image_path)

image = cv2.imread(image_path)

client = InferenceHTTPClient(
    api_url="https://serverless.roboflow.com",
    api_key=os.getenv("API_KEY"),
)
result = client.infer(image, model_id="your-project/1")

print(f"Top class: {result['top']} ({result['confidence']:.4f})")
```

{% hint style="info" %}
設定 `api_url` をデプロイ先に合わせて設定してください:

* `https://serverless.roboflow.com` は Serverless Hosted API 用です。
* `http://localhost:9001` ローカルの [Inference](https://inference.roboflow.com/) サーバー用です。
* あなたの [Dedicated Deployment](/roboflow/roboflow-jp/deploy/dedicated-deployments.md) の URL はプライベートエンドポイント用です。
  {% endhint %}


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