MobileNetV3-Large MIIL Model
| Property | Value |
|---|---|
| Parameter Count | 5.51M |
| License | Apache-2.0 |
| Paper | Searching for MobileNetV3 |
| Input Size | 224 x 224 |
| Training Data | ImageNet-21k-P, ImageNet-1k |
What is mobilenetv3_large_100.miil_in21k_ft_in1k?
This is an optimized version of MobileNetV3-Large, developed with a focus on mobile efficiency while maintaining high accuracy. The model has been pretrained on the extensive ImageNet-21k-P dataset and fine-tuned on ImageNet-1k, making it particularly robust for general image classification tasks.
Implementation Details
The model features a sophisticated architecture with 5.51M parameters and requires only 0.2 GMACs for inference. It processes images at 224x224 resolution and generates 4.4M activations during operation. The architecture implements the latest advancements in mobile-first neural network design, including optimized inverted residual blocks and squeeze-and-excitation modules.
- Efficient parameter utilization with only 5.51M parameters
- Optimized for mobile deployment
- Supports feature extraction and embedding generation
- Pre-trained on ImageNet-21k-P for enhanced transfer learning
Core Capabilities
- Image Classification with state-of-the-art accuracy
- Feature map extraction at multiple scales
- Generation of image embeddings
- Support for transfer learning tasks
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient architecture optimized for mobile devices while maintaining high accuracy through comprehensive pretraining on ImageNet-21k-P and fine-tuning on ImageNet-1k. It offers an excellent balance between model size and performance.
Q: What are the recommended use cases?
The model is ideal for mobile and edge device deployment in image classification tasks, feature extraction, and as a backbone for transfer learning in computer vision applications requiring efficient processing.
