deprecated-core VS yolov7

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⭐️ VDP on GitHub 👉 VDP documentation

Our team are building VDP, an open-source ETL tool for unstructured visual data. The idea is that with VDP, users can sync visual data from anywhere, transform the data into structured insights and load the insights into centralised warehouses or applications, just like how the modern data stack handles structured data.

Background: we're building VDP, an open-source visual data ETL tool to streamline the end-to-end visual data processing pipeline. If you're interested, check out our blog post: Introducing VDP, open-source visual data ETL.

If you want something off-the-shelf, try YoloV7.

The current dictionary is still in a beta state and has only been trained on 80 words (e.g. 'man', 'dog', 'car', 'keyboard', 'book', etc.; see list of words, see dataset). I used the object detection model Yolov7 (paper, all credits to them).

(Please note, this is a re-post of my original question here, I think this subreddit might be more appropriate for asking this question)At work, we use Unity, we have a project that needs object detection and classification. We decided to use this YOLO7 model (for non-technical reasons, It had to be the exact same model as the company does have pre-trained weights for this exact model). However, Unity only supports ONNX so I exported the model as an ONNX model, using the code provided in the repo:

Github Projects offering pretrained weights and train / run scripts. Example

The only project I found is this one that implements Yolov7

I'm wondering if there's an efficient way to visualize the image augmentations from the Yolov7 hyperparameters list here

yolov7: https://github.com/WongKinYiu/yolov7#performance