Welcome to Basic_Network, your go-to repository for replicating classic deep learning architectures in the computer vision realm. From image classification to object detection and segmentation, we’ve got it all covered. Our mission is to make state-of-the-art deep learning accessible, understandable, and functional for researchers and developers alike. Join us on our journey as we dive deeper into the realm of multimodal content learning.
- Classic Network Replications: Dive into our PyTorch implementations of iconic networks like ResNet and ViT.
- Multitask Learning: We go beyond image classification to include object detection and image segmentation.
- Clean Code: Our code is crafted for clarity and maintainability, complete with detailed annotations.
- Performance Benchmarks: We provide comparative performance metrics across standard datasets.
- Continuous Evolution: Stay tuned for the integration of cutting-edge models and multimodal learning approaches.
Ensure you have Python and PyTorch installed before you begin. Here’s how to get set up with Basic_Network:
git clone https://github.com/colorfulandcjy0806/Basic_Network.git
cd Basic_Network- Classification Models: Tap into our collection of models designed for pinpoint accuracy in image classification.
- Detection Models: Explore models that can detect and precisely locate objects within an image.
- Segmentation Models: Uncover models adept at distinguishing between different segments of an image, ideal for detailed analysis.
Dive into each domain with comprehensive guides and examples:
- Classification:
classification-models/ - Detection:
detection-models/ - Segmentation:
segmentation-models/
Your journey towards mastering deep learning in computer vision begins here. Let Basic_Network be your guide to the complex, fascinating world of neural networks and beyond.
- U-Net for Medical Image Segmentation: Adopt U-Net for a focus on segmentation in medical imaging, supporting a wider range of segmentation applications, particularly in biomedical imaging.
- Transformer Models (e.g., DETR, DeiT): Introduced models based on Transformers that have shown great performance in tasks like image classification and object detection.
- augmentation.py (April 2024): Implemented data augmentation techniques using the Albumentations library. This script is designed to enhance image datasets by applying various transformations such as flipping, rotation, scaling, brightness/contrast adjustment, and more.
Distributed under the Apache License 2.0 License. See LICENSE for more information.