TexasInstruments-EdgeAI/ResNet-Classification

ResNet for TI EdgeAI

Deep Residual Network for Image Classification

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Overview

ResNet-50 is a 50-layer deep convolutional neural network optimized for Texas Instruments MPU devices. This folder provides two production-ready ONNX variants that deliver industry-leading accuracy on ImageNet classification while maintaining efficient computation suitable for edge deployment.

This folder contains two distinct architectural generations of ResNet-50:

• resNet50 — ResNet-50 v1.5, the modern de-facto standard. Used by PyTorch (torchvision), TensorFlow, and most current frameworks. The stride-2 downsampling in each bottleneck block is applied in the 3×3 convolution rather than the 1×1, which improves accuracy with no added parameters. This is the version most practitioners encounter today.

• resnet50-v1 — ResNet-50 v1, the original architecture from He et al. (2016) as published in the ONNX Model Zoo (opset 7). Stride-2 is applied in the 1×1 convolution. Useful when strict reproducibility with the original paper or ONNX Model Zoo benchmarks is required.

The two variants differ only in where the stride-2 downsampling is placed inside each bottleneck block: moving the stride to the 3×3 conv (v1.5) preserves more spatial information before downsampling, which accounts for the ~1.2% accuracy gain over v1 at zero extra cost in parameters or FLOPs.

Which should I use? For new projects, prefer resNet50 (v1.5) — it is more accurate and is the implementation underlying most pre-trained weights available today. Use resnet50-v1 when you need exact compatibility with the original ONNX Model Zoo model or are comparing against v1 benchmarks.

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Model Variants

Model	Architecture	Params	Top-1 Accuracy	Validated Devices	Config
resNet50	ResNet-50 v1.5 (stride-2 in 3×3 conv)	~25.6M	76.15%	TDA4VH, TDA4VL, TDA4AEN	resnet50_config.yaml
resnet50-v1	ResNet-50 v1, original (stride-2 in 1×1 conv)	~25.6M	74.93%	TDA4VH, TDA4VL, TDA4AEN	resnet50-v1_config.yaml

Recommended for edge deployment: resNet50 (v1.5) — highest accuracy with the same compute cost (4.1 GigaMACs) as the original v1.

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Quick Start

Prerequisites

pip install onnx>=1.22.0 onnxruntime>=1.23.2

Export the Model

# Download and prepare the default model (resNet50, v1.5)
python prepare_model.py

# Download and prepare a specific variant via its .link file
python prepare_model.py --link-file resnet50-v1.onnx.link

# Skip download and only fix shapes on an already-downloaded model
python prepare_model.py --link-file resnet50.onnx.link --skip-download

# Use a custom input resolution
python prepare_model.py --link-file resnet50.onnx.link --height 256 --width 256

The script automatically:

• Parses the .link file to get the download URL and output filename
• Downloads the ONNX model from HuggingFace (unless --skip-download is set)
• Fixes dynamic input shapes to a static shape (default [1, 3, 224, 224])
• Runs ONNX shape inference and optional onnx-simplifier optimization
• Validates the resulting model and confirms all shapes are fixed

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/resnet50_config.yaml

Run Inference Benchmark - on device

cd /path/to/edgeai-tidlrunner
tidlrunner-cli infer --target_device J784S4 \
  --config_path /path/to/resnet50_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 this model, please cite:

@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision 
            and pattern recognition},
  pages={770--778},
  year={2016}
}

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🔗 Resources

Resource	Link
Paper	arXiv:1512.03385
Source (resNet50)	onnx-community/resnet-50-ONNX
Source (resnet50-v1)	onnxmodelzoo/resnet50-v1-7
edgeai-tidl-tools	GitHub
edgeai-tidlrunner	GitHub
EdgeAI SDK	Documentation

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Related Models

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ConvNeXt Modern CNN successor Higher accuracy

DINO (ResNet-50) Self-supervised ResNet No-label pre-training

ViT Vision Transformer Attention-based backbone

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