TexasInstruments-EdgeAI/DINOv2-Classification

DINOv2 for TI EdgeAI

Self-Supervised Vision Transformer Backbone for Image Classification

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Overview

DINOv2 (Self-Distillation with No Labels v2) is a self-supervised Vision Transformer pre-training method from Meta AI. It produces high-performance visual features using a purely self-supervised training regime on 142M images — no labels required during pre-training. The pretrained backbones are paired with a lightweight linear classification head for ImageNet-1K inference, outputting 1000-class logits.

All models use a ViT/14 patch size (14x14 patches) and are evaluated at 224x224 input resolution. Some variants add register tokens (arXiv:2309.16588) — extra learnable tokens that absorb the attention artifacts otherwise seen in patch-token feature maps, giving slightly better accuracy with the same backbone size.

See DINO for the original first-generation models.

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Model Variants

Model	Architecture	Params	GFLOPs	Top-1 Acc	Validated Devices	Config
dinov2_vits14_lc	ViT-S/14 distilled	21M	4.6	81.1%	TDA4VH	dinov2_vits14_lc_config.yaml
dinov2_vits14_reg_lc	ViT-S/14 distilled + registers	21M	4.6	80.9%	TDA4VH	dinov2_vits14_reg_lc_config.yaml
dinov2_vitb14_lc	ViT-B/14 distilled	86M	17.6	84.5%	TDA4VH	dinov2_vitb14_lc_config.yaml
dinov2_vitb14_reg_lc	ViT-B/14 distilled + registers	86M	17.6	84.6%	TDA4VH	dinov2_vitb14_reg_lc_config.yaml

Recommended for edge deployment: dinov2_vits14_lc (best accuracy/compute trade-off)

Note: ViT-L/14 and ViT-g/14 variants (dinov2_vitl14_lc, dinov2_vitg14_lc, and their _reg_lc counterparts) are excluded from this repo — they require ~16 GB+ RAM to export and are not suitable for edge (TIDL) deployment.

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Quick Start

Prerequisites

pip install torch torchvision onnx>=1.22.0 onnxruntime>=1.23.2 onnx-simplifier

Export the Model

# Export the default model (ViT-S/14 distilled)
python prepare_model.py

# Export a specific model variant
python prepare_model.py --model dinov2_vitb14_lc

# Export the registers variant
python prepare_model.py --model dinov2_vits14_reg_lc

# Export all edge-suitable models
python prepare_model.py --model all

# Re-run shape fixing on an already-exported ONNX
python prepare_model.py --model dinov2_vits14_lc --skip-export

The script automatically:

• Loads the pretrained backbone + linear classification head from PyTorch Hub (facebookresearch/dinov2)
• Exports to ONNX (opset 17) with a dynamic batch axis
• Fixes input shapes to [1, 3, 224, 224] and propagates shapes via ONNX shape inference
• Runs onnx-simplifier (onnxsim) and validates the final model with onnx.checker

Compile and Infer uing edgeai-tidlrunner

Note: Run the commands below from inside the tidlrunner directory (the cloned edgeai-tidlrunner repository), with --config_path pointing to this model's config file.

Compile using edgeai-tidlrunner - on PC

cd /path/to/edgeai-tidlrunner
tidlrunner-cli compile --target_device J784S4 \
  --config_path /path/to/dinov2_vits14_lc_config.yaml

Run Inference Benchmark - on device

cd /path/to/edgeai-tidlrunner
tidlrunner-cli infer --target_device J784S4 \
  --config_path /path/to/dinov2_vits14_lc_config.yaml

Compile and Infer using edgeai-tidl-tools (Advanced):

Follow the instructions at https://github.com/TexasInstruments/edgeai-tidl-tools

Deploy using edgeai-tidl-tools:

Deplyment can be done using edgeai-tidl-tools. For ONNX models, onnxruntime-tidl with TIDL acceleration can be used. Consult the documentation of edgeai-tidl-tools for more details.

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Citation

If you use these models, please cite:

@misc{oquab2023dinov2,
  title={DINOv2: Learning Robust Visual Features without Supervision},
  author={Oquab, Maxime and Darcet, Timothée and Moutakanni, Theo and others},
  journal={arXiv:2304.07193},
  year={2023}
}

@misc{darcet2023vitneedreg,
  title={Vision Transformers Need Registers},
  author={Darcet, Timothée and Oquab, Maxime and Mairal, Julien and Bojanowski, Piotr},
  journal={arXiv:2309.16588},
  year={2023}
}

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🔗 Resources

Resource	Link
Paper	arXiv:2304.07193
Registers Paper	arXiv:2309.16588
Source Code	facebookresearch/dinov2
edgeai-tidl-tools	GitHub
edgeai-tidlrunner	GitHub
EdgeAI SDK	Documentation
DINO	Predecessor model

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Related Models

DINO Predecessor First-generation self-supervised ViT

ViT Alternative backbone Supervised transformer

ResNet CNN backbone Lower compute

ConvNeXt Modern CNN Transformer-inspired design

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Maintained by: Texas Instruments EdgeAI Team
Last Updated: August 2026