TexasInstruments-EdgeAI/DINO-Classification

DINO for TI EdgeAI

Self-Supervised Vision Transformer Backbone for Image Classification

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Overview

DINO (Self-Distillation with No labels) is a self-supervised Vision Transformer pre-training method from Meta AI. The backbone models produce rich feature embeddings that achieve strong performance on ImageNet classification without any labels during pre-training.

These ONNX models include the full backbone + pretrained linear classification head, outputting 1000-class ImageNet logits [1, 1000]. Feature extraction follows DINO's eval_linear.py conventions:

• ViT-S models: CLS tokens from last 4 blocks concatenated → [B, 1536]
• ViT-B models: CLS token + averaged patch tokens (interleaved) → [B, 1536]
• ResNet-50: avgpool output → [B, 2048]

See DINOv2 for the improved second-generation models.

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Model Variants

Model	Architecture	Params	Linear Top-1	k-NN Top-1	Validated Devices	Config
dino_vits16	ViT-S/16	21M	77.0%	74.5%	TDA4VH	dino_vits16_config.yaml
dino_vits8	ViT-S/8	21M	79.7%	78.3%	TDA4VH	dino_vits8_config.yaml
dino_vitb16	ViT-B/16	85M	78.2%	76.1%	TDA4VH	dino_vitb16_config.yaml
dino_vitb8	ViT-B/8	85M	80.1%	77.4%	TDA4VH	dino_vitb8_config.yaml
dino_resnet50	ResNet-50	23M	75.3%	67.5%	TDA4VH	dino_resnet50_config.yaml

Recommended for edge deployment: dino_vits16 (best accuracy/compute trade-off)

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Quick Start

Prerequisites

pip install onnx>=1.22.0 onnxruntime>=1.23.2

Export the Model

# Export the default model (ViT-S/16)
python prepare_model.py

# Export a specific model variant
python prepare_model.py --model dino_vitb16

# Export all supported models
python prepare_model.py --model all

# Re-run shape fixing on an already-exported ONNX
python prepare_model.py --model dino_vits16 --skip-export

The script automatically:

• Loads pretrained backbone from PyTorch Hub (facebookresearch/dino:main)
• Downloads pretrained linear classification weights from Meta AI
• Combines backbone + linear head into a single classification model
• Exports to ONNX (opset 17) and fixes input shapes to [1, 3, 224, 224]
• Validates the model outputs [1, 1000] class logits

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/dino_vits16_config.yaml

Run Inference Benchmark - on device

cd /path/to/edgeai-tidlrunner
tidlrunner-cli infer --target_device J784S4 \
  --config_path /path/to/dino_vits16_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:

@inproceedings{caron2021emerging,
  title={Emerging Properties in Self-Supervised Vision Transformers},
  author={Caron, Mathilde and Touvron, Hugo and Misra, Ishan and
          J{\'e}gou, Herv{\'e} and Mairal, Julien and Bojanowski, Piotr
          and Joulin, Armand},
  booktitle={Proceedings of the IEEE/CVF International Conference
             on Computer Vision (ICCV)},
  year={2021}
}

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🔗 Resources

Resource	Link
Paper	arXiv:2104.14294
Source Code	facebookresearch/dino
edgeai-tidl-tools	GitHub
edgeai-tidlrunner	GitHub
EdgeAI SDK	Documentation
DINOv2	Improved successor

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Related Models

DINOv2 Improved DINO Higher accuracy

ViT-S/16 Recommended Best edge trade-off

ResNet-50 CNN backbone Lower compute

CLIP Vision-Language Zero-shot capable

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