Isaac 0.1
통합
perceptron/isaac-01
# Run the model; wait up to 20 s for the finished result in the same call
curl -X POST "https://api.doitong.com/v1/run/perceptron/isaac-01?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"image": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
}'
# Still "queued" or "running"? Poll with the id from the response:
curl "https://api.doitong.com/v1/requests/REQUEST_ID?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY"
const API = "https://api.doitong.com/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/perceptron/isaac-01?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"image": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
import os
import requests
API = "https://api.doitong.com/v1"
headers = {"Authorization": "Bearer " + os.environ["DOITONG_API_KEY"]}
payload = {
"image": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
}
# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/perceptron/isaac-01", params={"wait": 20}, json=payload, headers=headers)
request = r.json()
if not r.ok:
raise RuntimeError(request["error"]["code"] + ": " + request["error"]["message"])
# Not finished yet: long-poll until it is
while request["status"] in ("queued", "running"):
request = requests.get(API + "/requests/" + request["id"], params={"wait": 20}, headers=headers).json()
if request["status"] != "succeeded":
raise RuntimeError(request["error"])
print([o["url"] for o in request["outputs"]], request["cost"]["credits"], "API credits", "$" + request["cost"]["usd"])
# 1. Connect once (Claude Code; Cursor, VS Code, Claude Desktop: https://doitong.com/ko/developers/docs/mcp)
claude mcp add --transport http doitong-models https://api.doitong.com/v1/mcp \
--header "Authorization: Bearer $DOITONG_API_KEY"
# 2. Your agent calls the "run" tool with:
{
"model_id": "perceptron/isaac-01",
"input": {
"image": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
}
}
Doitong API를 통해 Isaac 0.1 모델(perceptron/isaac-01)을 호출합니다. 에이전트용 문서: https://doitong.com/ko/developers/models/perceptron/isaac-01.md
POST https://api.doitong.com/v1/run/perceptron/isaac-01?wait=20
Authorization: Bearer $DOITONG_API_KEY
Content-Type: application/json
{
"image": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
}
응답 상태가 "queued" 또는 "running"이면 "succeeded"가 될 때까지 GET https://api.doitong.com/v1/requests/REQUEST_ID?wait=20을 폴링하세요. 파일은 outputs[].url에 있습니다. POST https://api.doitong.com/v1/quote/perceptron/isaac-01에 동일한 본문을 보내면 실행 전에 정확한 가격을 반환합니다.
이 요청: ≈$0.001 1 API 크레딧 이 페이지에서 실행
새 API 키입니다. 지금만 표시되며 이미 아래 코드와 API 키 필드에 입력되어 있습니다. 안전하게 보관하세요. 브라우저에서 실행되는 코드에 절대 넣지 마세요.
첫 번째 결과가 여기에 표시됩니다
아래에서 요청을 작성하고 API 키로 실행하세요. 출력은 스토리지에 복사되며, 영구 보관하거나 7일 동안 무료로 보관할 수 있습니다.
요청 만들기
컨트롤은 모델 스키마에서 생성됩니다. 값을 변경하면 코드와 가격이 업데이트됩니다.
/run/perceptron/isaac-01?wait=20
견적 → $0.001
# Run the model; wait up to 20 s for the finished result in the same call
curl -X POST "https://api.doitong.com/v1/run/perceptron/isaac-01?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"image": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
}'
# Still "queued" or "running"? Poll with the id from the response:
curl "https://api.doitong.com/v1/requests/REQUEST_ID?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY"
const API = "https://api.doitong.com/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/perceptron/isaac-01?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"image": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
import os
import requests
API = "https://api.doitong.com/v1"
headers = {"Authorization": "Bearer " + os.environ["DOITONG_API_KEY"]}
payload = {
"image": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
}
# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/perceptron/isaac-01", params={"wait": 20}, json=payload, headers=headers)
request = r.json()
if not r.ok:
raise RuntimeError(request["error"]["code"] + ": " + request["error"]["message"])
# Not finished yet: long-poll until it is
while request["status"] in ("queued", "running"):
request = requests.get(API + "/requests/" + request["id"], params={"wait": 20}, headers=headers).json()
if request["status"] != "succeeded":
raise RuntimeError(request["error"])
print([o["url"] for o in request["outputs"]], request["cost"]["credits"], "API credits", "$" + request["cost"]["usd"])
{
"jsonrpc": "2.0",
"id": 1,
"method": "tools/call",
"params": {
"name": "run",
"arguments": {
"model_id": "perceptron/isaac-01",
"input": {
"image": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
},
"wait_seconds": 20
}
}
}
Isaac 0.1 정보
`image_url`과 `prompt`를 받아 `response_style`로 지정한 형식의 응답을 반환합니다. Text는 캡션과 설명에 적합하고, box, point 및 polygon 스타일은 위치 정보를 추가합니다. 결과에는 생성된 출력, 부분 출력 플래그, 오류 메시지(있는 경우)와 사용량 정보가 포함됩니다.
매개변수
표준 이름이나 네이티브 이름 중 하나만 보내세요. 표준 이름
image_url필수
image
prompt필수
response_style선택 사항
파일 매개변수에는 공개 https:// URL, 최대 10 MB의 data: URI 또는 업로드의 file_url을 사용할 수 있습니다. 파일
출력
{
"id": "req_8k2m4q7x1z9c3v5b6n0p2r4t",
"object": "request",
"model": "perceptron/isaac-01",
"status": "succeeded",
"created_at": "2026-10-20T12:00:00Z",
"started_at": "2026-10-20T12:00:02Z",
"completed_at": "2026-10-20T12:00:09Z",
"queue_position": null,
"cost": {
"credits": 1,
"usd": "0.001",
"charged": 1,
"refunded": 0
},
"input": {
"image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
},
"output": {
"output": "To determine which car is trying to park into the garage, we need to carefully observe the positions and movements of the vehicles in the image.\n\n1. **Identify the Vehicles**: \n - There are three vehicles visible: a green truck, an orange car, and a brown car.\n - The green truck is positioned in front of the garage entrance.\n - The orange car is parked to the left of the green truck.\n - The brown car is parked to the right of the green truck.\n\n2. **Analyze the Positions**:\n - The green truck is directly in front of the garage entrance, suggesting it is in the process of moving towards the entrance.\n - The orange car is parked parallel to the garage but is not in the path of the green truck.\n - The brown car is also parked parallel to the garage but is not in the path of the green truck.\n\n3. **Determine the Action**:\n - Given the position of the green truck directly in front of the garage entrance, it is most likely that the green truck is trying to park into the garage.\n - The orange and brown cars are already parked and do not appear to be in motion or attempting to park.\n\nTherefore, the car that is trying to park into the garage is the green truck.",
"partial": false,
"error": "string",
"usage": {
"completion_tokens": 1,
"prompt_tokens": 1,
"total_tokens": 1
}
},
"outputs": [
{
"type": "file",
"url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
"content_type": "image/jpeg",
"file_size": 1843200,
"stored": true
}
],
"error": null,
"metrics": {
"inference_seconds": 6.8
},
"urls": {
"self": "https://api.doitong.com/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t",
"cancel": "https://api.doitong.com/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t/cancel"
}
}
출력 필드
- output
- string모델이 생성한 결과입니다.
- partial
- boolean출력이 불완전한지 나타냅니다.
- error
- string | null오류가 발생한 경우 오류를 설명하는 메시지입니다.
- usage
- object {completion_tokens, prompt_tokens, total_tokens} | null사용량 세부 정보입니다.
더 많은 모델: 비전
AI 에이전트용: 이 페이지의 Markdown 버전: https://doitong.com/ko/developers/models/perceptron/isaac-01.md. 모든 모델 및 가이드: https://doitong.com/developers/llms.txt