Anomaly Detection in Crop Datasets using Medoids

### For anomaly detection
1. The first pipeline is uploading (crops) dataset to Qdrant's collection.
2. **This is the second pipeline, to set up cluster (class) centres in this Qdrant collection & cluster (class) threshold scores.**
3. The third one is the anomaly detection tool, which takes any image as input and uses all preparatory work done with Qdrant (crops) collection.

### To recreate it
You'll have to upload [crops]({{ $env.WEBHOOK_URL }} dataset from Kaggle to your own Google Storage bucket, and re-create APIs/connections to [Qdrant Cloud]({{ $env.WEBHOOK_URL }} (you can use **Free Tier** cluster), Voyage AI API & Google Cloud Storage

**In general, pipelines are adaptable to any dataset of images**