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ConvNeXt
Ambarella/ConvNeXt
ConvNeXt is a machine learning model from Ambarella. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for pytorch.
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Repo size
100 MB
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At a glance
- Library
- pytorch
- Model type
- llama
- Access
- Public
- Created
- Dec 22, 2025
- Updated
- Sep 11, 2026
- SHA
- e8da48f0
Model card
From the Hugging Face model README
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-Tiny
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_Tiny
- Original Weight: ConvNeXt_Tiny_Weights.IMAGENET1K_V1
- Dataset: ImageNet
- 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 | X7 | Model_Link |
| ConvNeXt-T | CV7 | Model_Link |
| ConvNeXt-T | CV72 | Model_Link |
| ConvNeXt-T | CV75 | Model_Link |
- Library
- pytorch
- Type
- llama
- Created
- Dec 22, 2025
- Updated
- Sep 11, 2026