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In this computer vision tutorial video, we will explore various methods for utilizing pre-trained models to enhance your personal data sets. Our primary focus will be on fine-tuning, a highly versatile technique, while also discussing alternative approaches. You'll discover that employing pre-trained models is particularly beneficial when dealing with limited data or computational resources for training your own model
Topics Covered
✅Overview of Pre-Trained Model Use Cases
✅Download and Extract the Dataset
✅Dataset and Training Configuration
✅Create Train and Validation Datasets
✅Create the Test Dataset
✅Modeling VGG-16 (for Fine-Tuning)
✅Model Evaluation
❓FAQ on Keras/TensorFlow
How do you fine-tune a Pretrained model in Keras?
What is fine-tuning in Keras code?
How can I use pre-trained models in Keras?
What is the difference between fine-tuning and pre-training?
How do I load a pre-trained model in TensorFlow?
What is fine-tuning a model?
⭐️ Time Stamps:⭐️
00:00-00:26: Introduction
00:26-01:55: Preview
01:55-03:00: Pre-Trained Models
03:00-05:10: Training a Model from Scratch
05:10-06:54: Transfer Learning
06:54-09:40: Fine Tuning
09:40-10:31: Downloading the Dataset
10:31-11:58: Python data classes
11:58-16:15: Create Training & Dataset Validation Objects
16:15-17:38: Creating a Test Dataset
17:38-18:27: Pandas Data frame
18:27-21:25: Mapping
21:25-21:48: Image Paths
21:48-22:15: Combining Image Paths
22:15-23:57: Convenience Function
23:57-24:20: Displaying Images
24:20-24:48: Modelling VGG-16
24:48-26:35: Loading the VGG-16
26:35-28:28: Constructing the Model
28:28-30:44: Compiling the Model
30:44-32:32: Training Results
32:32-35:25: Model Evaluation
35:25-36:15: Conclusion
Resources:
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5 июл 2024
