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package:dataset-imagenet-preprocessed-using-opencv (v1.0.0)
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to automatically download and install different components (models, data sets, libraries, frameworks, tools)
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across evolving platforms.
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Creation date: 2019-07-25
Source: GitHub
cID: 1dc07ee0f4742028:4932bbdd2ac7a17b
Don't hesitate to get in touch if you encounter any issues or would like to discuss this community project!
Source: GitHub
cID: 1dc07ee0f4742028:4932bbdd2ac7a17b
Description
- Automation framework: CK
- Development repository: ck-env
- Source: GitHub
- Tags: image-classification,dataset,imagenet,2012,val,preprocessed,using-opencv
- Support for host OS: any
- Support for target OS: any
- How to get the stable version from this portal via the CK client:
pip install cbench
cb download package:dataset-imagenet-preprocessed-using-opencv --version=1.0.0 --all
ck install package:dataset-imagenet-preprocessed-using-opencv
ck help package
- How to get the development version:
pip install ck
ck pull repo:ck-env
ck install package:dataset-imagenet-preprocessed-using-opencv
ck help package
- Useful CK automation actions (CLI with the Python CK API and JSON IO):
ck show env
ck find env:{ID from above}
ck virtual env:{ID from above}
- CLI and Python API: module:package
- Variations:
- universal
- for-mobilenet
- for-resnet
- crop.1000
- crop.875
- external_file
- first.20
- full
- last.20
- side.128
- side.160
- side.192
- side.224
- side.256
- Useful CK automation actions (CLI with the Python CK API and JSON IO):
ReadMe
Installation
$ ck install package --tags=dataset,imagenet,preprocessed,using-opencv
Details
Summary of preprocessing methods
The table below summarizes the available methods.
| Preprocessing method | OpenCV universal | OpenCV for ResNet | OpenCV for MobileNet |
|---|---|---|---|
| Additional tags | universal |
for-resnet |
for-mobilenet |
| Supported models | ResNet, MobileNet | ResNet only | MobileNet only |
| Supported platforms | x86i | x86 | x86 |
| Data format | rgb8 (int8) | rgbf32 (float32) | rgbf32 (float32) |
| Data size | 7.1G | 29G | 29G |
Accuracy on the ImageNet 2012 validation set
The table below shows the accuracy on the ImageNet 2012 validation set (50,000 images) of the MLPerf Inference v0.5 image classification models measured - via TensorFlow (C++)
| Model | Metric | OpenCV universal | OpenCV for ResNet | OpenCV for MobileNet |
|---|---|---|---|---|
| ResNet | Top1 | 0.76442 | 0.76456 | N/A |
| Top5 | 0.93074 | 0.93016 | N/A | |
| MobileNet non-quantized | Top1 | 0.71676 | N/A | 0.71676 |
| Top5 | 0.90118 | N/A | 0.90118 | |
| MobileNet quantized | Top1 | 0.70700 | N/A | 0.70694 |
| Top5 | 0.89594 | N/A | 0.89594 |
| Model | Metric | OpenCV universal | OpenCV for ResNet | OpenCV for MobileNet |
|---|---|---|---|---|
| ResNet | Top1 | 0.76442 | 0.76456 | N/A |
| Top5 | 0.93074 | 0.93016 | N/A | |
| MobileNet non-quantized | Top1 | 0.71676 | N/A | 0.71676 |
| Top5 | 0.90118 | N/A | 0.90118 | |
| MobileNet quantized | Top1 | 0.70762 | N/A | N/A (bug?) |
| Top5 | 0.89266 | N/A | N/A (bug?) |
Additional notes
-
ResNet achieves 0.76450/0.93058 with TF-C++/TFLite, universal OpenCV preprocessing and the green channel mean of 116.6.
-
MobileNet quantized used to achieve 0.70776 with TFLite and universal OpenCV preprocessing with area interpolation.
Files
- .cm/desc.json (3 bytes)
- .cm/info.json (327 bytes)
- .cm/meta.json (4129 bytes)
- README.md (3223 bytes)
- preprocess_image_dataset.py (6955 bytes)
- shell_wrapper.sh (129 bytes)