Working with images, embedding model - Voyage AI.
1. This is the first pipeline to upload (crops) dataset to Qdrant's collection.
2. The second pipeline is to set up cluster (class) centres in this Qdrant collection & cluster (class) threshold scores.
3. The third is the anomaly detection tool, which takes any image as input and uses all preparatory work done with Qdrant (crops) collection.
1. This is the first pipeline to upload (lands) dataset to Qdrant's collection.
2. The second is the KNN classifier tool, which takes any image as input and classifies it based on queries to the Qdrant (lands) collection.
You'll have to upload crops and lands datasets from Kaggle to your own Google Storage bucket, and re-create APIs/connections to Qdrant Cloud (you can use Free Tier cluster), Voyage AI API & Google Cloud Storage
In general, pipelines are adaptable to any dataset of images.
This template imports dataset images from Google Could Storage, creates Voyage AI embeddings for them in batches, and uploads them to Qdrant in batches. In this particular template, we work with crops dataset. However, it's analogous to uploading lands dataset, and in general, it's adaptable to any dataset consisting of image URLs (as the following pipelines are).
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