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"name": "[2/2] KNN classifier (lands dataset)",
"tags": [
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"name": "classifier",
"createdAt": "2024-12-08T22:08:15.968Z",
"updatedAt": "2024-12-09T19:25:04.113Z"
}
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"name": "Embed image",
"type": "n8n-nodes-base.httpRequest",
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"parameters": {
"url": "https://api.voyageai.com/v1/multimodalembeddings",
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"jsonBody": "={{n{n "inputs": [n {n "content": [n {n "type": "image_url",n "image_url": $json.imageURLn }n ]n }n ],n "model": "voyage-multimodal-3",n "input_type": "document"n}n}}",
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"url": "={{ $json.qdrantCloudURL }}/collections/{{ $json.collectionName }}/points/query",
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"language": "python",
"pythonCode": "from collections import Counternninput_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)nnreturn [{n "json": {n "result": majority_vote_two_most_common n }n}]n"
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"assignments": [
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"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 }}"
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{
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{
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"assignments": [
{
"id": "880838bf-2be2-4f5f-9417-974b3cbee163",
"name": "=limitKNN",
"type": "number",
"value": "={{ $json.result.points.length}}"
},
{
"id": "5fff2bea-f644-4fd9-ad04-afbecd19a5bc",
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"assignments": [
{
"id": "46ceba40-fb25-450c-8550-d43d8b8aa94c",
"name": "imageURL",
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{
"id": "980663f6-9d7d-4e88-87b9-02030882472c",
"operator": {
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"operation": "gt"
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"leftValue": "={{ $json.result.length }}",
"rightValue": 1
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{
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"leftValue": "={{ $json.result[0][1] }}",
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{
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"parameters": {
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"assignments": {
"assignments": [
{
"id": "de66070d-5e74-414e-8af7-d094cbc26f62",
"name": "ImageEmbedding",
"type": "array",
"value": "={{ $json.data[0].embedding }}"
},
{
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},
{
"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"
}
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},
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{
"id": "d1bc4edc-37d2-43ac-8d8b-560453e68d1f",
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"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,
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],
"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-tooln### 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)nn* 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 loopnHere we're [querying](https://api.qdrant.tech/api-reference/search/query-points) Qdrant, getting **limitKNN** nearest neighbours to our image , parsing their classes from payloads (images were pre-labeled & uploaded with their labels to Qdrant) & calculating the most frequent class name . If there is a tie 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 .nIf there is no tie, or we have already checked 100 neighbours, we exit the loop 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 Voten"
},
"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) classificationn1. 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.**nn### To recreate itnYou'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 Storagenn**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": [
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{
"node": "Qdrant variables + embedding + KNN neigbours",
"type": "main",
"index": 0
}
]
]
},
"Query Qdrant": {
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[
{
"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": [
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{
"node": "Query Qdrant",
"type": "main",
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"Execute Workflow Trigger": {
"main": [
[
{
"node": "Image Test URL",
"type": "main",
"index": 0
}
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"Propagate loop variables": {
"main": [
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{
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"type": "main",
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}
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"Qdrant variables + embedding + KNN neigbours": {
"main": [
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{
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}