Главная
Study mode:
on
1
Introduction
2
Outline
3
Object Detection
4
Prediction Problem
5
Mean Average Precision
6
Why should we care
7
Transformer Decoder Architecture
8
Positional Encoding
9
Decoder
10
Prediction
11
Training
12
DebtR Performance
13
DebtR Drawbacks
14
Deformable Attention
15
Multiscale Features
16
Performance
17
State of the Art
18
Training Models
19
Defining PiTorch Trial
20
Defining Experiment Config
21
Determining Web UI
22
HP Search
23
Other Metrics
24
Tensorboard
25
Automatic Fault Tolerance
26
Save Model
27
Output
28
Results
29
Conclusion
Description:
Explore object detection with transformers in this comprehensive 32-minute talk from Databricks. Learn the fundamentals of object detection, including key concepts and techniques, before delving into cutting-edge methods that utilize transformers to streamline the detection pipeline. Discover the main ideas behind DETR and Deformable DETR approaches, and gain an overview of Determined AI's deep learning platform capabilities, focusing on effortless distributed training. Master the process of training object detection models at scale and serving them using MLflow. Grasp essential topics such as mean average precision, transformer decoder architecture, positional encoding, and deformable attention. Follow along as the speaker demonstrates defining PyTorch trials, configuring experiments, and utilizing Determined's web UI for hyperparameter search, automatic fault tolerance, and model saving. By the end of this talk, acquire the knowledge to implement advanced object detection techniques and deploy them effectively in various applications, from medical image analysis to autonomous driving. Read more

Object Detection with Transformers - From Training to Deployment

Databricks
Add to list
0:00 / 0:00