convmixer

**ConvMixer** is the **patch based convolutional architecture that keeps ViT style patch embedding but uses depthwise and pointwise convolutions for mixing** - it demonstrates that much of the performance gain comes from patch tokenization and modern training recipes, not only from attention. **What Is ConvMixer?** - **Definition**: A model that starts with patch embedding convolution, then repeats depthwise convolution for spatial mixing and pointwise convolution for channel mixing. - **Patch First Design**: Treats image as coarse tokens from the first layer, similar to ViT patchify stage. - **Convolutional Mixer**: Uses separable convolutions instead of attention for token interaction. - **Residual Blocks**: Includes skip connections and activation normalization for stable deep training. **Why ConvMixer Matters** - **Fair Comparison**: Shows how strong patchification plus recipe tuning can make simple conv models highly competitive. - **Hardware Practicality**: Convolution kernels are mature and highly optimized on many platforms. - **Data Efficiency**: Often trains well on moderate data compared with data hungry transformer baselines. - **Interpretability**: Depthwise filters are easier to inspect than dense attention weights. - **Deployment Speed**: Inference stacks for conv operators are widely available and optimized. **ConvMixer Building Blocks** **Patch Embedding Layer**: - Large stride convolution converts raw pixels into patch tokens. - Sets token granularity and compute budget. **Depthwise Spatial Mixing**: - Per-channel spatial convolution captures local structure. - Repeated blocks expand receptive field with depth. **Pointwise Channel Mixing**: - One by one convolution fuses channel information. - Acts similarly to channel MLP in Mixer models. **How It Works** **Step 1**: Apply patch embedding convolution to convert image into low resolution token feature map. **Step 2**: Repeat depthwise plus pointwise conv blocks with residual paths, then global pool and classify. **Tools & Platforms** - **timm**: Ready to use ConvMixer models and checkpoints. - **TensorRT and OpenVINO**: Excellent support for separable conv inference. - **PyTorch**: Straightforward to tune patch size, depth, and width. ConvMixer is **a strong reminder that patch tokenization and training strategy can rival more complex attention models** - it offers a practical high speed baseline with familiar convolution operators.