tf_mixnet_l.in1k
| Property | Value |
|---|---|
| Parameter Count | 7.38M |
| Model Type | Image Classification |
| License | Apache-2.0 |
| Paper | MixConv: Mixed Depthwise Convolutional Kernels |
| Image Size | 224 x 224 |
What is tf_mixnet_l.in1k?
tf_mixnet_l.in1k is a sophisticated image classification model that implements the MixNet architecture, originally trained in TensorFlow and successfully ported to PyTorch by Ross Wightman. This model represents an innovative approach to computer vision, utilizing mixed depthwise convolutional kernels to achieve efficient and accurate image classification.
Implementation Details
The model features a carefully balanced architecture with 7.38M parameters and requires 0.6 GMACs for inference. It processes images at 224x224 resolution and utilizes mixed depthwise convolutional kernels, which combine different kernel sizes in a single convolution operation. The model was trained on the ImageNet-1k dataset, making it suitable for general-purpose image classification tasks.
- Efficient parameter utilization with mixed convolutions
- Optimized for both accuracy and computational efficiency
- Supports feature extraction and embedding generation
- Compatible with PyTorch ecosystem through timm library
Core Capabilities
- Image Classification: Primary task with ImageNet-1k categories
- Feature Map Extraction: Provides multi-scale feature maps
- Embedding Generation: Can generate image embeddings for downstream tasks
- Transfer Learning: Suitable for fine-tuning on custom datasets
Frequently Asked Questions
Q: What makes this model unique?
This model's uniqueness lies in its implementation of mixed depthwise convolutional kernels, which allow it to capture features at multiple scales simultaneously while maintaining computational efficiency. The model achieves a good balance between model size (7.38M parameters) and performance.
Q: What are the recommended use cases?
The model is well-suited for general image classification tasks, feature extraction, and as a backbone for transfer learning. It's particularly effective when deployment efficiency is a concern while maintaining good accuracy. The model can be used for both inference and as a feature extractor for downstream tasks.
