towhee VS examples

As another commenter noted, Milvus is overkill and a "bit much" if you're learning/playing.

A good intro to the field with progression towards a full Milvus implementation could be starting with towhee[0] (which is also supported by Milvus).

towhee has an example to do exactly what you want with CLIP[1].

[0] - https://towhee.io/

[1] - https://github.com/towhee-io/examples/tree/main/image/text_i...

The description of this is kind of confusing but I think the easiest way to understand it is that it is a data processing pipeline of sorts. Take unstructured data and apply transformation and computation. A similar project to this is Towhee (https://github.com/towhee-io/towhee). This project tries to simplify unstructured data processing and provides pretrained models and pipelines from their hub.

Milvus also has incredible flexibility when it comes to choosing an indexing strategy, and we also have a library specifically meant to help vectorize a variety of data called Towhee (https://github.com/towhee-io/towhee).

Benchmarking the models with towhee is as simple as:

The project's homepage is https://github.com/towhee-io/towhee, and you can find more about towhee by going through the documents.

I made a tutorial for building an image search engine with python. The code example is as simple as 10 lines of code, using Towhee and Milvus To put images into the search engine:

Traditionally, I've done this through PyTorch by adding a hook, but this requires knowledge of the model itself (i.e. model arch and layer names). I found https://github.com/Hironsan/awesome-embedding-models but it didn't provide many CV-focused open-source projects. There's also https://github.com/towhee-io/towhee which is great but more targeted towards application development.

You can check the result from the tutorial.

As another commenter noted, Milvus is overkill and a "bit much" if you're learning/playing.

A good intro to the field with progression towards a full Milvus implementation could be starting with towhee[0] (which is also supported by Milvus).

towhee has an example to do exactly what you want with CLIP[1].

[0] - https://towhee.io/

[1] - https://github.com/towhee-io/examples/tree/main/image/text_i...

Usually this is done in three steps. The first step is using a neural network to create a bounding box around the object, then generating vector embeddings of the object, and then using similarity search on vector embeddings.

The first step is accomplished by training a detection model to generate the bounding box around your object, this can usually be done by finetuning an already trained detection model. For this step the data you would need is all the images of the object you have with a bounding box created around it, the version of the object doesnt matter here.

The second step involves using a generalized image classification model thats been pretrained on generalized data (VGG, etc.) and a vector search engine/vector database. You would start by using the image classification model to generate vector embeddings (https://frankzliu.com/blog/understanding-neural-network-embe...) of all the different versions of the object. The more ground truth images you have, the better, but it doesn't require the same amount as training a classifier model. Once you have your versions of the object as embeddings, you would store them in a vector database (for example Milvus: https://github.com/milvus-io/milvus).

Now whenever you want to detect the object in an image you can run the image through the detection model to find the object in the image, then run the sliced out image of the object through the vector embedding model. With this vector embedding you can then perform a search in the vector database, and the closest results will most likely be the version of the object.

Hopefully this helps with the general rundown of how it would look like. Here is an example using Milvus and Towhee https://github.com/towhee-io/examples/tree/3a2207d67b10a246f....

Disclaimer: I am a part of those two open source projects.

I have shown how to Build an Image Search Engine in Minutes in the previous tutorial. Here is another one for how to optimize the algorithm, feed it with large-scale image datasets, and deploy it as a micro-service.

The full tutorial is at https://github.com/towhee-io/examples/blob/main/image/reverse_image_search/build_image_search_engine.ipynb