| Property | Value |
|---|---|
| Parameter Count | 194M |
| Model Type | Image Classification |
| License | CC-BY-NC-4.0 |
| Top-1 Accuracy | 83.35% |
| GMACs | 36.3 |
What is resnext101_32x16d.fb_swsl_ig1b_ft_in1k?
This is a powerful ResNeXt architecture model developed by Facebook Research, leveraging semi-weakly supervised learning on a massive Instagram-1B dataset before being fine-tuned on ImageNet-1k. The model represents a significant advancement in transfer learning, combining the benefits of large-scale pretraining with efficient architecture design.
Implementation Details
The model is built on the ResNeXt architecture, featuring a 101-layer deep network with cardinality of 32 and width of 16d. It utilizes grouped 3x3 convolutions in its bottleneck design, alongside ReLU activations and efficient shortcut connections.
- Uses 7x7 convolution with pooling in initial layers
- Implements 1x1 convolution shortcut downsample
- Features grouped 3x3 bottleneck convolutions
- Optimized for 224x224 input images
Core Capabilities
- High-accuracy image classification (83.35% top-1)
- Efficient feature extraction for transfer learning
- Robust performance on diverse image types
- Balanced computational efficiency (36.3 GMACs)
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its semi-weakly supervised pretraining on Instagram-1B dataset, which provides it with robust feature learning capabilities before ImageNet fine-tuning. The 32x16d architecture offers an excellent balance between model capacity and computational efficiency.
Q: What are the recommended use cases?
The model excels in complex image classification tasks, transfer learning applications, and feature extraction scenarios. It's particularly well-suited for applications requiring high accuracy and robust feature representation, though users should consider the computational requirements of 194M parameters.