YOLOv7: End-to-end Annotations, Training and Inference using Roboflow
Channel: Devopedia
Date: 2022-09-13

We are going to talk about how to build up a whole pipeline end to end for object detection, from creating an annotation for a custom dataset on RoboFlow to how to load it in a free Kaggle environment. YOLOv7 was released in a paper titled "Trainable bags-of-freebies sets new state-of-the-art for real-time object detectors". We will show how to train our own custom YOLOv7 model for free with integrated weights and biases to monitor the performance of different parameters set up for different model training. Finally, we are going to show how to deploy it for free on Hugging Face Spaces for inference.

Speakers:
Manu Joseph, Head of Applied Research @ Thoucentric, Creator of PyTorch Tabular
Owais Ahmad, Data Scientist @ Thoucentric

Links:
1. Model Inference🤖 - https://huggingface.co/spaces/owaiskha9654/Custom_Yolov7
2. 🚀Training Yolov7 on Kaggle - https://www.kaggle.com/code/owaiskhan9654/training-yolov7-on-kaggle-on-custom-dataset
3. Weight and Biases 🐝 - https://wandb.ai/owaiskhan9515/YOLOR
4. HuggingFace 🤗 Model Repo - https://huggingface.co/owaiskha9654/Yolov7_Custom_Object_Detection