dla102.in1k
| Property | Value |
|---|---|
| Parameter Count | 33.3M |
| Model Type | Image Classification |
| Framework | PyTorch (timm) |
| License | BSD-3-Clause |
| Paper | Deep Layer Aggregation |
What is dla102.in1k?
dla102.in1k is an implementation of the Deep Layer Aggregation (DLA) architecture, specifically designed for image classification tasks. This model represents a sophisticated approach to feature hierarchy aggregation, trained on the ImageNet-1k dataset. With 33.3M parameters and 7.2 GMACs compute complexity, it strikes a balance between model capacity and computational efficiency.
Implementation Details
The model utilizes a hierarchical structure that aggregates features across layers, enabling better semantic and spatial information flow. It operates on 224x224 pixel images and produces feature maps at various scales, making it suitable for both classification and feature extraction tasks.
- Activation Memory: 14.2M
- Computational Complexity: 7.2 GMACs
- Input Resolution: 224x224 pixels
- Feature Map Capabilities: Produces multi-scale features from 112x112 to 7x7
Core Capabilities
- Image Classification with 1000 classes (ImageNet)
- Feature Map Extraction at multiple scales
- Image Embedding Generation
- Transfer Learning Support
Frequently Asked Questions
Q: What makes this model unique?
DLA102 stands out for its deep layer aggregation approach, which creates hierarchical networks that can capture both spatial and semantic information effectively. The architecture's ability to aggregate features across different scales makes it particularly effective for complex visual recognition tasks.
Q: What are the recommended use cases?
The model is well-suited for image classification tasks, particularly when working with the ImageNet dataset. It can also be effectively used for feature extraction, transfer learning, and as a backbone for more complex computer vision tasks like object detection or segmentation.
