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DETR: End-to-End Object Detection with Transformers
A new method that views object detection as a direct set prediction problem.
object-detection image-segmentation panoptic-segmentation transformers
Getting started with JAX (MLPs, CNNs & RNNs)
Learn the building blocks of JAX and use them to build some standard Deep Learning architectures (MLP, CNN, RNN, etc.).
jax xla autograd tpu
T5 fine-tuning
A colab notebook to showcase how to fine-tune T5 model on various NLP tasks (especially non text-2-text tasks with text-2-text approach)
natural-language-processing transformers text-2-text t5
MedicalZoo PyTorch
A pytorch-based deep learning framework for multi-modal 2D/3D medical image segmentation
medical-image-segmentation volumetric-segmentation medical-image-proccessing deep-learning
Finetuning Transformers with JAX + Haiku
Walking through a port of the RoBERTa pre-trained model to JAX + Haiku, then fine-tuning the model to solve a downstream task.
jax haiku roberta transformers
Object Detection with RetinaNet
Implementing RetinaNet: Focal Loss for Dense Object Detection.
object-detection retinanet keras tensorflow
Customizing What Happens in Fit()
How to leverage the convenient features of fit() with a custom training loop.
tensorflow keras training tutorial
Training a pets detector model with TFOD API (TF 2)
In this notebook, we will be training a custom object detection model using the latest TensorFlow Object Detection (TFOD) API which is based on TensorFlow ...
computer-vision deep-learning object-detection notebook
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