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CNNs process images with learned filters applied to local neighborhoods; a standard Vision Transformer (ViT) splits an image into patches and uses self-attention to combine information among patch tokens. CNNs build in locality and shared spatial patterns, while ViTs offer more direct interactions across image regions. Neither is always better: results depend on the task, training data, pretraining, compute, and evaluation method.
How a CNN processes an image
A convolutional neural network applies learned kernels—small filters—to an image or to intermediate feature maps. The same kernel weights are reused as the filter moves across positions, allowing a pattern to be detected in different parts of the image.
Early layers commonly respond to local features such as edges and textures. Later layers combine those activations into representations of larger structures. As layers accumulate, a CNN can use information from increasingly broad areas of the image; it is not limited to local information overall.
This design builds in a useful spatial bias: nearby pixels often have related structure, and a feature can remain meaningful when its location shifts. Convolution and weight sharing support translation-related equivariance, but they do not guarantee that every CNN is invariant to every transformation.
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How a standard Vision Transformer processes an image
- Divide the image into patches. A standard ViT splits the input into fixed-size patches. The original ViT paper describes this patch-based approach as treating image patches as a sequence, rather than processing the image only through a convolutional stack. Read the original ViT paper.
- Turn each patch into a token. Each patch is flattened or otherwise represented, then projected into a vector embedding.
- Add position information. Positional information lets the model distinguish where tokens originated in the image; without it, the sequence alone would not identify each patch’s location.
- Mix information with transformer blocks. Self-attention lets a token’s update depend on other tokens across the image, while feed-forward layers further process the representations. This allows interactions between distant patches at a transformer block, rather than relying only on a small local neighborhood at that layer.
Patch size, input resolution, attention design, and model architecture affect compute and how much image detail is retained. “ViT” therefore describes a family of designs, not one fixed implementation or cost profile.
The key difference: built-in assumptions and information mixing
| Aspect | CNN | Standard ViT |
|---|---|---|
| Basic operation | Applies shared learned filters over local neighborhoods. | Embeds image patches as tokens and uses self-attention to mix information among tokens. |
| Spatial structure | Locality and weight sharing are built into the architecture. | Uses positional information, with less image-specific structure built into the basic architecture. |
| Building broader context | Combines features across layers, expanding the area of the image that can influence later representations. | Attention can connect tokens from different image regions within a transformer block. |
| What this does not guarantee | Equivariance does not mean invariance to every transformation, and a CNN is not restricted to local information across its full depth. | Flexible token interactions do not by themselves make a ViT more accurate or more data-efficient. |
The practical distinction is an architectural prior, not a verdict about quality. CNNs favor shared local patterns; a vanilla ViT can learn broader relationships among patches. The value of either design depends on whether its strengths fit the data and task.
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Which is better for image classification?
There is no universal winner. A ViT can perform strongly with an appropriate scale and training setup, but a result cannot be attributed to architecture alone when models differ in pretraining, data, or fine-tuning. The original ViT results establish capability under their particular training regime, not that every ViT beats every CNN.
Training scale can matter. A 2022 survey reports that ViT-L’s ImageNet test accuracy was 13 percentage points lower when trained only on ImageNet than when pretrained on JFT, which the survey identifies as a dataset of 300 million images. This is a historical comparison of those specific training setups—not a current benchmark or a prediction for other models and recipes. See the 2022 survey.
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Comparative results also depend on what is measured. A 2024 ICML paper examines supervised and CLIP-pretrained models beyond ImageNet accuracy, underscoring why one benchmark score is not enough to select an architecture. Read the 2024 comparison.
How to compare two models fairly
When choosing between actual CNN and ViT options, compare the complete training and deployment setups—not just the architecture labels.
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- Task and output: Identify whether you need classification, detection, segmentation, or another output. A classification result alone does not establish performance on a different task.
- Data regime: Record dataset size, quality, domain match, and whether each model is trained from scratch or fine-tuned.
- Pretraining: Compare pretraining datasets and objectives. If those differ, do not credit a performance gain solely to the architecture.
- Compute and deployment: Consider parameter count, FLOPs, memory, input resolution, and measured latency on the hardware where the model will run. FLOPs are only an imperfect proxy for actual latency.
- Evaluation quality: Use consistent data splits, metrics, augmentation, and tuning effort; account for robustness needs where relevant.
- Transfer: Check whether the learned representation works on the intended downstream data, rather than relying only on a headline benchmark.
Research comparing CNN and transformer representations has specifically examined transferability, reinforcing that model selection should follow the intended use rather than a single accuracy ranking. Read the ICCV Workshop comparison.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Hybrid models combine convolution and attention
The choice is not strictly CNN or ViT. Hybrid architectures bring convolutional operations into transformer designs to retain useful spatial structure while using attention. CvT, for example, introduces convolutional token embedding and convolutional projections. Its authors describe their proposal as intended to improve performance and efficiency by combining these design elements; that claim belongs to their paper’s experiments, not to hybrids as a whole. Read the CvT paper.
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Other work likewise incorporates convolution designs into visual transformers to encourage local, low-level feature extraction while retaining long-range dependency modeling. Those designs do not make every hybrid superior to every standalone CNN or ViT. Read the study on convolution designs in visual transformers.
Quick Recap
Common misconceptions
- “CNNs only see local information.” A convolutional layer uses local neighborhoods, but stacked layers build broader representations.
- “Attention means a ViT understands the whole image.” Attention provides a mechanism for combining token information; it is not evidence of human-like understanding.
- “ViTs are always better.” Performance depends on the model, data, pretraining, training procedure, task, and evaluation.
- “The comparison is always CNN versus a plain ViT.” Transformer designs can include local or hierarchical structure, and hybrid models combine convolution with attention.
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