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This project demonstrates the use of TensorFlow Object Detection API (along with GCP ML Engine) to automatically detect Red Blood Cells (RBCs), White Blood Cells (WBCs), and Platelets in each image taken via microscopic image readings

License: MIT License

PureBasic 0.01% Jupyter Notebook 99.73% Python 0.26%
deep-learning google-cloud-platform ml-engine object-detection tensorflow-object-detection-api

blood-cell-detection-using-tfod-api's Introduction

My name is Sayak Paul! 👾

  • 🔭 I work on 🧨 diffusion models at Hugging Face 🤗
  • 🌱 I’m interested in the area of representation learning (self-supervision, semi-supervision, model robustness).
  • 👯 I’m always open to meaningful collaborations.
  • ⚡ Fun fact: I love watching crime and action thrillers (The Silence of the Lambs being an all-time favorite one).
  • 🙃 Recepient of the Google Open Source Peer Bonus Award (2020, 2021, and 2022). Also received the TensorFlow Top Contributor Award 2021.
  • 📫 More details - sayak.dev.

Following are some of my favorite repositories that I have contributed to and/or contribute to.

blood-cell-detection-using-tfod-api's People

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adnanrahin asukumari allandiego dr-alok-tiwari hounddogdan ben-norton lucasbatista57

blood-cell-detection-using-tfod-api's Issues

how to generate mAP for training dataset?

Hi @sayakpaul , Thanks very much for sharing your repository. I have noticed that you have put the tensorboard graph during your presentation. I would like to get some suggestions from you.

  1. how do we get the training metrics like mAP value for every 100 epochs?
  2. how to pass the validation data while training the model, so we can see how well the model perform with validation data?

It would be helpful you can provide us with some information.

Thanks very much :)

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