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opensecura / ml-models-public / 2e99517d38fab6519e2335f92117bbbe7320e620
commit2e99517d38fab6519e2335f92117bbbe7320e620[log] [tgz]
authorCindy Liu <hcindyl@google.com>Mon Aug 15 13:51:10 2022 -0700
committerCindy Liu <hcindyl@google.com>Mon Aug 15 13:51:10 2022 -0700
tree7b3376e8b0127581d267b57733ce380191a90d18
parent50114bc52cc9aeefc8b79e1363f6b0a0e7b0fe20 [diff]
ml-models-public: Update codegen API for IREE release 237

Fix 242568306
Fix 241254202

IREE commits:
https://github.com/iree-org/iree/pull/10096
https://github.com/iree-org/iree/pull/10094

Bug: 242568306
Bug: 241254202
Change-Id: I8bf6c1cb78ffff4bcff6cbc1d5f59fa6d96a074c
10 files changed
tree: 7b3376e8b0127581d267b57733ce380191a90d18
  1. float_models/
  2. quant_models/
  3. test_data/
  4. CMakeLists.txt
  5. README.md
README.md

Public ML model zoo

This is the model zoo for public models used in Shodan vector core examples.

Model discription

person_detection.tflite

The person presence detection quantized model from https://github.com/tensorflow/tflite-micro/blob/main/tensorflow/lite/micro/models/person_detect.tflite

mobilenet_v1_0.25_224_float.tflite

Mobilenet V1 float-point model from https://tfhub.dev/tensorflow/lite-model/mobilenet_v1_0.25_224/1/default/1?lite-format=tflite

mobilenet_v1_0.25_224_quant.tflite

Mobilenet V1 quantized model from https://tfhub.dev/tensorflow/lite-model/mobilenet_v1_0.25_224_quantized/1/default/1?lite-format=tflite

mobilenet_v2_1.0_224_quant.tflite

Mobilenet V2 quantized model from https://tfhub.dev/tensorflow/lite-model/mobilenet_v2_1.0_224_quantized/1/default/1?lite-format=tflite

Visualize the model

For tflite models, use the web-based visualization tool to inspect the file. The tool supports drag and drop or file GUI.

Test data

Test data for sample model inputs are in the “test_data” directory. Currently, only test data for the Human Presence Sensor model are included.

hps_0-6.jpg

Test image files for Human Presence Sensor model, from ChromeOS codebase.

Model executables

For each model, the corresponding model executable under iree_exec can be built with the .c/.h files using sw/vec_iree project as library. They can be run as unit tests via lit framework using Renode or QEMU.