A ConvNet for the 2020s
Paper • 2201.03545 • Published • 2
Configuration Parsing Warning:Config file config.json cannot be fetched (too big)
ConvNeXt revisits and modernizes convolutional neural network design by incorporating architectural insights from Vision Transformers, such as large kernels, simplified blocks, and improved normalization, while retaining convolutional efficiency.
Original paper: A ConvNet for the 2020s, Liu et al., 2022
ConvNeXt-T
This model uses the ConvNeXt-Tiny variant, a lightweight configuration that delivers strong accuracy with relatively low computational cost. It is well suited for high-resolution image classification and as a general-purpose backbone for detection and segmentation tasks where CNN efficiency is preferred.
Model Configuration:
- Reference implementation: ConvNeXt_T
- Original Weight: ConvNeXt_Tiny_Weights.IMAGENET1K_V1
- Resolution: 3x224x224
- Support Cooper version:
- Cooper SDK: [2.5.4]
- Cooper Foundry: [2.3]
| Model | Device | Model Link |
|---|---|---|
| ConvNeXt-T | N1-655 | Model_Link |
| ConvNeXt-T | CV7 | Model_Link |
| ConvNeXt-T | CV72 | Model_Link |
| ConvNeXt-T | CV75 | Model_Link |
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