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: [2.5.2]
- Cooper Foundry: [2.2]
| Model | Device | Model Link |
|---|---|---|
| ConvNeXt-T | N1-655 | Model_Link |
| ConvNeXt-T | CV72 | Model_Link |
| ConvNeXt-T | CV75 | Model_Link |
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