license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
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.
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.
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
.linkfile to get the download URL and output filename - Downloads the ONNX model from HuggingFace (unless
--skip-downloadis set) - Fixes dynamic input shapes to a static shape (default
[1, 3, 224, 224]) - Runs ONNX shape inference and optional
onnx-simplifieroptimization - Validates the resulting model and confirms all shapes are fixed
Compile and Infer uing edgeai-tidlrunner
Note: Run the commands below from inside the
tidlrunnerdirectory (the cloned edgeai-tidlrunner repository), with--config_pathpointing 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.
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}
}
🔗 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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ConvNeXt Modern CNN successor Higher accuracy |
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ViT Vision Transformer Attention-based backbone |
Maintained by: Texas Instruments EdgeAI Team
Last Updated: August 2026