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📋 Optimisez la classification d'images avec KNN et n8n
💡 Description
Ce workflow n8n utilise un classificateur KNN pour analyser et classifier des images satellites. Grâce à l'intégration de services tels que Voyage AI et Qdrant, il extrait les caractéristiques des images, identifie les voisins les plus proches, et applique un vote majoritaire pour déterminer la classe de l'image. Ce processus automatisé améliore l'efficacité en réduisant le temps nécessaire pour analyser manuellement les images tout en garantissant une précision de 93,24% sans réglages supplémentaires.
📈 Impact & ROI: Réduit considérablement le temps d'analyse d'images tout en augmentant la précision, offrant ainsi un retour sur investissement significatif grâce à l'automatisation.
🚀 Fonctionnalités Clés
- ✅ Automatisation avancée de la classification d'images
- ✅ Intégration fluide avec Voyage AI et Qdrant
- ✅ Amélioration de la précision d'analyse d'images
- ✅ Résolution automatique des égalités dans le vote
📊 Architecture Technique
18
Nodes
Nodes
9
Connexions
Connexions
3
Services
Services
🔌 Services Intégrés
Voyage AIQdrantGoogle Cloud Storage
🔧 Composition du Workflow
| Node | Type | Description |
|---|---|---|
| Embed image | httpRequest | Requête HTTP vers une API externe |
| Query Qdrant | httpRequest | Requête HTTP vers une API externe |
| Majority Vote | code | Traitement des données |
| Increase limitKNN | set | Traitement des données |
| Propagate loop variables | set | Traitement des données |
| Image Test URL | set | Traitement des données |
| Return class | set | Traitement des données |
| Check tie | if | Condition logique pour router le flux |
| Qdrant variables + embedding + KNN neigbours | set | Traitement des données |
| Sticky Note | stickyNote | Traitement des données |
| Sticky Note1 | stickyNote | Traitement des données |
| Execute Workflow Trigger | executeWorkflowTrigger | Traitement des données |
| Sticky Note2 | stickyNote | Traitement des données |
| Sticky Note3 | stickyNote | Traitement des données |
| Sticky Note4 | stickyNote | Traitement des données |
| Sticky Note5 | stickyNote | Traitement des données |
| Sticky Note6 | stickyNote | Traitement des données |
| Sticky Note10 | stickyNote | Traitement des données |
📖 Guide d'Implémentation
- Import du workflow: Téléchargez le fichier JSON et importez-le dans votre instance n8n
- Configuration des credentials: Configurez les accès pour chaque service utilisé
- Personnalisation: Adaptez les paramètres selon vos besoins spécifiques
- Test: Exécutez le workflow en mode test pour vérifier le bon fonctionnement
- Activation: Activez le workflow pour une exécution automatique
🏷️ Tags
Structure JSON
Voir le code JSON complet
{
"id": "itzURpN5wbUNOXOw",
"meta": {
"instanceId": "205b3bc06c96f2dc835b4f00e1cbf9a937a74eeb3b47c99d0c30b0586dbf85aa"
},
"name": "[2\/2] KNN classifier (lands dataset)",
"tags": [
{
"id": "QN7etptCmdcGIpkS",
"name": "classifier",
"createdAt": "2024-12-08T22:08:15.968Z",
"updatedAt": "2024-12-09T19:25:04.113Z"
}
],
"nodes": [
{
"id": "33373ccb-164e-431c-8a9a-d68668fc70be",
"name": "Embed image",
"type": "n8n-nodes-base.httpRequest",
"position": [
-140,
-240
],
"parameters": {
"url": "https:\/\/api.voyageai.com\/v1\/multimodalembeddings",
"method": "POST",
"options": [],
"jsonBody": "={{\n{\n \"inputs\": [\n {\n \"content\": [\n {\n \"type\": \"image_url\",\n \"image_url\": $json.imageURL\n }\n ]\n }\n ],\n \"model\": \"voyage-multimodal-3\",\n \"input_type\": \"document\"\n}\n}}",
"sendBody": true,
"specifyBody": "json",
"authentication": "genericCredentialType",
"genericAuthType": "httpHeaderAuth"
},
"credentials": {
"httpHeaderAuth": {
"id": "Vb0RNVDnIHmgnZOP",
"name": "Voyage API"
}
},
"typeVersion": 4.2
},
{
"id": "58adecfa-45c7-4928-b850-053ea6f3b1c5",
"name": "Query Qdrant",
"type": "n8n-nodes-base.httpRequest",
"position": [
440,
-240
],
"parameters": {
"url": "={{ $json.qdrantCloudURL }}\/collections\/{{ $json.collectionName }}\/points\/query",
"method": "POST",
"options": [],
"jsonBody": "={{\n{\n \"query\": $json.ImageEmbedding,\n \"using\": \"voyage\",\n \"limit\": $json.limitKNN,\n \"with_payload\": true\n}\n}}",
"sendBody": true,
"specifyBody": "json",
"authentication": "predefinedCredentialType",
"nodeCredentialType": "qdrantApi"
},
"credentials": {
"qdrantApi": {
"id": "it3j3hP9FICqhgX6",
"name": "QdrantApi account"
}
},
"typeVersion": 4.2
},
{
"id": "258026b7-2dda-4165-bfe1-c4163b9caf78",
"name": "Majority Vote",
"type": "n8n-nodes-base.code",
"position": [
840,
-240
],
"parameters": {
"language": "python",
"pythonCode": "from collections import Counter\n\ninput_json = _input.all()[0]\npoints = input_json['json']['result']['points']\nmajority_vote_two_most_common = Counter([point[\"payload\"][\"landscape_name\"] for point in points]).most_common(2)\n\nreturn [{\n \"json\": {\n \"result\": majority_vote_two_most_common \n }\n}]\n"
},
"typeVersion": 2
},
{
"id": "e83e7a0c-cb36-46d0-8908-86ee1bddf638",
"name": "Increase limitKNN",
"type": "n8n-nodes-base.set",
"position": [
1240,
-240
],
"parameters": {
"options": [],
"assignments": {
"assignments": [
{
"id": "0b5d257b-1b27-48bc-bec2-78649bc844cc",
"name": "limitKNN",
"type": "number",
"value": "={{ $('Propagate loop variables').item.json.limitKNN + 5}}"
},
{
"id": "afee4bb3-f78b-4355-945d-3776e33337a4",
"name": "ImageEmbedding",
"type": "array",
"value": "={{ $('Qdrant variables + embedding + KNN neigbours').first().json.ImageEmbedding }}"
},
{
"id": "701ed7ba-d112-4699-a611-c0c134757a6c",
"name": "qdrantCloudURL",
"type": "string",
"value": "={{ $('Qdrant variables + embedding + KNN neigbours').first().json.qdrantCloudURL }}"
},
{
"id": "f5612f78-e7d8-4124-9c3a-27bd5870c9bf",
"name": "collectionName",
"type": "string",
"value": "={{ $('Qdrant variables + embedding + KNN neigbours').first().json.collectionName }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "8edbff53-cba6-4491-9d5e-bac7ad6db418",
"name": "Propagate loop variables",
"type": "n8n-nodes-base.set",
"position": [
640,
-240
],
"parameters": {
"options": [],
"assignments": {
"assignments": [
{
"id": "880838bf-2be2-4f5f-9417-974b3cbee163",
"name": "=limitKNN",
"type": "number",
"value": "={{ $json.result.points.length}}"
},
{
"id": "5fff2bea-f644-4fd9-ad04-afbecd19a5bc",
"name": "result",
"type": "object",
"value": "={{ $json.result }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "6fad4cc0-f02c-429d-aa4e-0d69ebab9d65",
"name": "Image Test URL",
"type": "n8n-nodes-base.set",
"position": [
-320,
-240
],
"parameters": {
"options": [],
"assignments": {
"assignments": [
{
"id": "46ceba40-fb25-450c-8550-d43d8b8aa94c",
"name": "imageURL",
"type": "string",
"value": "={{ $json.query.imageURL }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "f02e79e2-32c8-4af0-8bf9-281119b23cc0",
"name": "Return class",
"type": "n8n-nodes-base.set",
"position": [
1240,
0
],
"parameters": {
"options": [],
"assignments": {
"assignments": [
{
"id": "bd8ca541-8758-4551-b667-1de373231364",
"name": "class",
"type": "string",
"value": "={{ $json.result[0][0] }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "83ca90fb-d5d5-45f4-8957-4363a4baf8ed",
"name": "Check tie",
"type": "n8n-nodes-base.if",
"position": [
1040,
-240
],
"parameters": {
"options": [],
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "980663f6-9d7d-4e88-87b9-02030882472c",
"operator": {
"type": "number",
"operation": "gt"
},
"leftValue": "={{ $json.result.length }}",
"rightValue": 1
},
{
"id": "9f46fdeb-0f89-4010-99af-624c1c429d6a",
"operator": {
"type": "number",
"operation": "equals"
},
"leftValue": "={{ $json.result[0][1] }}",
"rightValue": "={{ $json.result[1][1] }}"
},
{
"id": "c59bc4fe-6821-4639-8595-fdaf4194c1e1",
"operator": {
"type": "number",
"operation": "lte"
},
"leftValue": "={{ $('Propagate loop variables').item.json.limitKNN }}",
"rightValue": 100
}
]
}
},
"typeVersion": 2.2
},
{
"id": "847ced21-4cfd-45d8-98fa-b578adc054d6",
"name": "Qdrant variables + embedding + KNN neigbours",
"type": "n8n-nodes-base.set",
"position": [
120,
-240
],
"parameters": {
"options": [],
"assignments": {
"assignments": [
{
"id": "de66070d-5e74-414e-8af7-d094cbc26f62",
"name": "ImageEmbedding",
"type": "array",
"value": "={{ $json.data[0].embedding }}"
},
{
"id": "58b7384d-fd0c-44aa-9f8e-0306a99be431",
"name": "qdrantCloudURL",
"type": "string",
"value": "=https:\/\/152bc6e2-832a-415c-a1aa-fb529f8baf8d.eu-central-1-0.aws.cloud.qdrant.io"
},
{
"id": "e34c4d88-b102-43cc-a09e-e0553f2da23a",
"name": "collectionName",
"type": "string",
"value": "=land-use"
},
{
"id": "db37e18d-340b-4624-84f6-df993af866d6",
"name": "limitKNN",
"type": "number",
"value": "=10"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "d1bc4edc-37d2-43ac-8d8b-560453e68d1f",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
-940,
-120
],
"parameters": {
"color": 6,
"width": 320,
"height": 540,
"content": "Here we're classifying existing types of satellite imagery of land types:\n- 'agricultural',\n- 'airplane',\n- 'baseballdiamond',\n- 'beach',\n- 'buildings',\n- 'chaparral',\n- 'denseresidential',\n- 'forest',\n- 'freeway',\n- 'golfcourse',\n- 'harbor',\n- 'intersection',\n- 'mediumresidential',\n- 'mobilehomepark',\n- 'overpass',\n- 'parkinglot',\n- 'river',\n- 'runway',\n- 'sparseresidential',\n- 'storagetanks',\n- 'tenniscourt'\n"
},
"typeVersion": 1
},
{
"id": "13560a31-3c72-43b8-9635-3f9ca11f23c9",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
-520,
-460
],
"parameters": {
"color": 6,
"content": "I tested this KNN classifier on a whole `test` set of a dataset (it's not a part of the collection, only `validation` + `train` parts). Accuracy of classification on `test` is **93.24%**, no fine-tuning, no metric learning."
},
"typeVersion": 1
},
{
"id": "8c9dcbcb-a1ad-430f-b7dd-e19b5645b0f6",
"name": "Execute Workflow Trigger",
"type": "n8n-nodes-base.executeWorkflowTrigger",
"position": [
-520,
-240
],
"parameters": [],
"typeVersion": 1
},
{
"id": "b36fb270-2101-45e9-bb5c-06c4e07b769c",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1080,
-520
],
"parameters": {
"width": 460,
"height": 380,
"content": "## KNN classification workflow-tool\n### This n8n template takes an image URL (as anomaly detection tool does), and as output, it returns a class of the object on the image (out of land types list)\n\n* An image URL is received via the Execute Workflow Trigger, which is then sent to the Voyage.ai Multimodal Embeddings API to fetch its embedding.\n* The image's embedding vector is then used to query Qdrant, returning a set of X similar images with pre-labeled classes.\n* Majority voting is done for classes of neighbouring images.\n* A loop is used to resolve scenarios where there is a tie in Majority Voting (for example, we have 5 \"forest\" and 5 \"beach\"), and we increase the number of neighbours to retrieve.\n* When the loop finally resolves, the identified class is returned to the calling workflow."
},
"typeVersion": 1
},
{
"id": "51ece7fc-fd85-4d20-ae26-4df2d3893251",
"name": "Sticky Note3",
"type": "n8n-nodes-base.stickyNote",
"position": [
120,
-40
],
"parameters": {
"height": 200,
"content": "Variables define another Qdrant's collection with landscapes (uploaded similarly as the crops collection, don't forget to switch it with your data) + amount of neighbours **limitKNN** in the database we'll use for an input image classification."
},
"typeVersion": 1
},
{
"id": "7aad5904-eb0b-4389-9d47-cc91780737ba",
"name": "Sticky Note4",
"type": "n8n-nodes-base.stickyNote",
"position": [
-180,
-60
],
"parameters": {
"height": 80,
"content": "Similarly to anomaly detection tool, we're embedding input image with the Voyage model"
},
"typeVersion": 1
},
{
"id": "d3702707-ee4a-481f-82ca-d9386f5b7c8a",
"name": "Sticky Note5",
"type": "n8n-nodes-base.stickyNote",
"position": [
440,
-500
],
"parameters": {
"width": 740,
"height": 200,
"content": "## Tie loop\nHere we're [querying](https:\/\/api.qdrant.tech\/api-reference\/search\/query-points) Qdrant, getting **limitKNN** nearest neighbours to our image <*Query Qdrant node*>, parsing their classes from payloads (images were pre-labeled & uploaded with their labels to Qdrant) & calculating the most frequent class name <*Majority Vote node*>. If there is a tie <*check tie node*> in 2 most common classes, for example, we have 5 \"forest\" and 5 \"harbor\", we repeat the procedure with the number of neighbours increased by 5 <*propagate loop variables node* and *increase limitKNN node*>.\nIf there is no tie, or we have already checked 100 neighbours, we exit the loop <*check tie node*> and return the class-answer."
},
"typeVersion": 1
},
{
"id": "d26911bb-0442-4adc-8511-7cec2d232393",
"name": "Sticky Note6",
"type": "n8n-nodes-base.stickyNote",
"position": [
1240,
160
],
"parameters": {
"height": 80,
"content": "Here, we extract the name of the input image class decided by the Majority Vote\n"
},
"typeVersion": 1
},
{
"id": "84ffc859-1d5c-4063-9051-3587f30a0017",
"name": "Sticky Note10",
"type": "n8n-nodes-base.stickyNote",
"position": [
-520,
80
],
"parameters": {
"color": 4,
"width": 540,
"height": 260,
"content": "### KNN (k nearest neighbours) classification\n1. The first pipeline is uploading (lands) dataset to Qdrant's collection.\n2. **This is the KNN classifier tool, which takes any image as input and classifies it based on queries to the Qdrant (lands) collection.**\n\n### To recreate it\nYou'll have to upload [lands](https:\/\/www.kaggle.com\/datasets\/apollo2506\/landuse-scene-classification) dataset from Kaggle to your own Google Storage bucket, and re-create APIs\/connections to [Qdrant Cloud](https:\/\/qdrant.tech\/documentation\/quickstart-cloud\/) (you can use **Free Tier** cluster), Voyage AI API & Google Cloud Storage\n\n**In general, pipelines are adaptable to any dataset of images**\n"
},
"typeVersion": 1
}
],
"active": false,
"pinData": {
"Execute Workflow Trigger": [
{
"json": {
"query": {
"imageURL": "https:\/\/storage.googleapis.com\/n8n-qdrant-demo\/land-use\/images_train_test_val\/test\/buildings\/buildings_000323.png"
}
}
}
]
},
"settings": {
"executionOrder": "v1"
},
"versionId": "c8cfe732-fd78-4985-9540-ed8cb2de7ef3",
"connections": {
"Check tie": {
"main": [
[
{
"node": "Increase limitKNN",
"type": "main",
"index": 0
}
],
[
{
"node": "Return class",
"type": "main",
"index": 0
}
]
]
},
"Embed image": {
"main": [
[
{
"node": "Qdrant variables + embedding + KNN neigbours",
"type": "main",
"index": 0
}
]
]
},
"Query Qdrant": {
"main": [
[
{
"node": "Propagate loop variables",
"type": "main",
"index": 0
}
]
]
},
"Majority Vote": {
"main": [
[
{
"node": "Check tie",
"type": "main",
"index": 0
}
]
]
},
"Image Test URL": {
"main": [
[
{
"node": "Embed image",
"type": "main",
"index": 0
}
]
]
},
"Increase limitKNN": {
"main": [
[
{
"node": "Query Qdrant",
"type": "main",
"index": 0
}
]
]
},
"Execute Workflow Trigger": {
"main": [
[
{
"node": "Image Test URL",
"type": "main",
"index": 0
}
]
]
},
"Propagate loop variables": {
"main": [
[
{
"node": "Majority Vote",
"type": "main",
"index": 0
}
]
]
},
"Qdrant variables + embedding + KNN neigbours": {
"main": [
[
{
"node": "Query Qdrant",
"type": "main",
"index": 0
}
]
]
}
}
}
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