activatedgeek/LeNet-5 use ONNX for inference by 
Ciro Santilli 35 Updated 2025-03-28 +Created 1970-01-01
Ciro Santilli 35 Updated 2025-03-28 +Created 1970-01-01
Number 9 drawn with mouse on GIMP by Ciro Santilli (2023)
Note that:
- the images must be drawn with white on black. If you use black on white, it the accuracy becomes terrible. This is a good very example of brittleness in AI systems!
- images must be converted to 32x32 for
lenet.onnx, as that is what training was done on. The training step converted the 28x28 images to 32x32 as the first thing it does before training even starts
We can try the code adapted from thenewstack.io/tutorial-using-a-pre-trained-onnx-model-for-inferencing/ at lenet/infer.py:and it works pretty well! The program outputs:as desired.
cd lenet
cp ~/git/LeNet-5/lenet.onnx .
wget -O 9.png https://raw.githubusercontent.com/cirosantilli/media/master/Digit_9_hand_drawn_by_Ciro_Santilli_on_GIMP_with_mouse_white_on_black.png
./infer.py 9.png9We can also try with images directly from Extract MNIST images.and the accuracy is great as expected.
infer_mnist.py lenet.onnx mnist_png/out/testing/1/*.pngDeep learning is the name artificial neural networks basically converged to in the 2010s/2020s.
It is a bit of an unfortunate as it suggests something like "deep understanding" and even reminds one of AGI, which it almost certainly will not attain on its own. But at least it sounds good.
What is backpropagation really doing? by 3Blue1Brown (2017)
Source. Good hand-wave intuition, but does not describe the exact algorithm.mlcommons.org/en/ Their homepage is not amazingly organized, but it does the job.
Benchmark focused on deep learning. It has two parts:Furthermore, a specific network model is specified for each benchmark in the closed category: so it goes beyond just specifying the dataset.
Results can be seen e.g. at:
Those URLs broke as of 2025 of course, now you have to click on their Tableau down to the 2.1 round and there's no fixed URL for it:
And there are also separate repositories for each:
E.g. on mlcommons.org/en/training-normal-21/ we can see what the the benchmarks are:
| Dataset | Model |
|---|---|
| ImageNet | ResNet |
| KiTS19 | 3D U-Net |
| OpenImages | RetinaNet |
| COCO dataset | Mask R-CNN |
| LibriSpeech | RNN-T |
| Wikipedia | BERT |
| 1TB Clickthrough | DLRM |
| Go | MiniGo |
Instructions at:
Ubuntu 22.10 setup with tiny dummy manually generated ImageNet and run on ONNX:
sudo apt install pybind11-dev
git clone https://github.com/mlcommons/inference
cd inference
git checkout v2.1
virtualenv -p python3 .venv
. .venv/bin/activate
pip install numpy==1.24.2 pycocotools==2.0.6 onnxruntime==1.14.1 opencv-python==4.7.0.72 torch==1.13.1
cd loadgen
CFLAGS="-std=c++14" python setup.py develop
cd -
cd vision/classification_and_detection
python setup.py develop
wget -q https://zenodo.org/record/3157894/files/mobilenet_v1_1.0_224.onnx
export MODEL_DIR="$(pwd)"
export EXTRA_OPS='--time 10 --max-latency 0.2'
tools/make_fake_imagenet.sh
DATA_DIR="$(pwd)/fake_imagenet" ./run_local.sh onnxruntime mobilenet cpu --accuracyLast line of output on P51, which appears to contain the benchmark resultswhere presumably
TestScenario.SingleStream qps=58.85, mean=0.0138, time=0.136, acc=62.500%, queries=8, tiles=50.0:0.0129,80.0:0.0137,90.0:0.0155,95.0:0.0171,99.0:0.0184,99.9:0.0187qps means queries per second, and is the main results we are interested in, the more the better.Running:produces a tiny ImageNet subset with 8 images under
tools/make_fake_imagenet.shfake_imagenet/.fake_imagenet/val_map.txt contains:val/800px-Porsche_991_silver_IAA.jpg 817
val/512px-Cacatua_moluccensis_-Cincinnati_Zoo-8a.jpg 89
val/800px-Sardinian_Warbler.jpg 13
val/800px-7weeks_old.JPG 207
val/800px-20180630_Tesla_Model_S_70D_2015_midnight_blue_left_front.jpg 817
val/800px-Welsh_Springer_Spaniel.jpg 156
val/800px-Jammlich_crop.jpg 233
val/782px-Pumiforme.JPG 285- 817: 'sports car, sport car',
- 89: 'sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita',
TODO prepare and test on the actual ImageNet validation set, README says:
Prepare the imagenet dataset to come.
Since that one is undocumented, let's try the COCO dataset instead, which uses COCO 2017 and is also a bit smaller. Note that his is not part of MLperf anymore since v2.1, only ImageNet and open images are used. But still:
wget https://zenodo.org/record/4735652/files/ssd_mobilenet_v1_coco_2018_01_28.onnx
DATA_DIR_BASE=/mnt/data/coco
export DATA_DIR="${DATADIR_BASE}/val2017-300"
mkdir -p "$DATA_DIR_BASE"
cd "$DATA_DIR_BASE"
wget http://images.cocodataset.org/zips/val2017.zip
wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip
unzip val2017.zip
unzip annotations_trainval2017.zip
mv annotations val2017
cd -
cd "$(git-toplevel)"
python tools/upscale_coco/upscale_coco.py --inputs "$DATA_DIR_BASE" --outputs "$DATA_DIR" --size 300 300 --format png
cd -Now:fails immediately with:The more plausible looking:first takes a while to preprocess something most likely, which it does only one, and then fails:
./run_local.sh onnxruntime mobilenet cpu --accuracyNo such file or directory: '/path/to/coco/val2017-300/val_map.txt./run_local.sh onnxruntime mobilenet cpu --accuracy --dataset coco-300Traceback (most recent call last):
File "/home/ciro/git/inference/vision/classification_and_detection/python/main.py", line 596, in <module>
main()
File "/home/ciro/git/inference/vision/classification_and_detection/python/main.py", line 468, in main
ds = wanted_dataset(data_path=args.dataset_path,
File "/home/ciro/git/inference/vision/classification_and_detection/python/coco.py", line 115, in __init__
self.label_list = np.array(self.label_list)
ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (5000, 2) + inhomogeneous part.TODO!
Deep learning is mostly matrix multiplication by Ciro Santilli 35 Updated 2025-03-28 +Created 1970-01-01
Ciro Santilli 35 Updated 2025-03-28 +Created 1970-01-01 Why Ciro Santilli refers to himself in the third person by Ciro Santilli 35 Updated 2025-03-28 +Created 1970-01-01
Ciro Santilli 35 Updated 2025-03-28 +Created 1970-01-01Because when this gets converted to a OurBigBook.com page, it will be easier for people to copy paragraphs/fork and write a canonical page about Ciro.
What do you do when creating a pull request? Do you say "I", which is not true because Ciro did not say that, or do you say "John Doe thinks" bla bla?
And because his name is awesome! :-) Just kidding.
This became a micro-meme in 4chan:Correction: cirosantilli.com is not Ciro Santili's resume. It is your life.
- 2020-09-21 archive.vn/wip/Zz7fx (original) "ITT: weird sites you found by accident" a comment reads:
cirosantilli.com/ this is some guys resume who repeats his own name well over 1,000 times.
- 2020-04-30 archive.is/LgDbK (original) "Interesting Website thread" a comment reads:cirosantilli.com/ What is even this?"a guy who says his name over 500 times in his resume."
The most important thing this project provides appears to be the
.onnx file format, which represents ANN models, pre-trained or not.Deep learning frameworks can then output such
.onnx files for interchangeability and serialization.Some examples:
- activatedgeek/LeNet-5 produces a trained
.onnxfrom PyTorch - MLperf v2.1 ResNet can use
.onnxas a pre-trained model
The cool thing is that ONNX can then run inference in an uniform manner on a variety of devices without installing the deep learning framework used for. It's a bit like having a kind of portable executable. Neat.
Pinned article: ourbigbook/introduction-to-the-ourbigbook-project
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Figure 5. . You can also edit articles on the Web editor without installing anything locally. Video 3. Edit locally and publish demo. Source. This shows editing OurBigBook Markup and publishing it using the Visual Studio Code extension.Video 4. OurBigBook Visual Studio Code extension editing and navigation demo. Source. - Internal cross file references done right:
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