* Add TPIP used by Arm NN
* Add SqueezeNet to CaffeSupport.md
* Add ResNet v2 50 to TensorFlowSupport.md
* Update tested networks in TensorFlowLiteSupported.md
Signed-off-by: Narumol Prangnawarat <narumol.prangnawarat@arm.com>
Change-Id: I15927ea600a3dfffee082933d32f235f3940730e
This enables machine processing of license information based on the SPDX License Identifiers that are available here: http://spdx.org/licenses/
+TPIP used by Arm NN:
+
+| Name | License (SPDX ID) |
+|------|-------------------|
+| half | MIT |
+| stb | MIT |
+
### Contributions
The Arm NN project welcomes contributions. For more details on contributing to Arm NN see the [Contributing page](https://mlplatform.org/contributing/) on the [MLPlatform.org](https://mlplatform.org/) website, or see the [Contributor Guide](ContributorGuide.md).
- Yolov1_tiny.
- Lenet.
- MobileNetv1.
+- SqueezeNet v1.0 and SqueezeNet v1.1
The Arm NN SDK supports the following machine learning layers for Caffe networks:
* DeepSpeaker
+* [DeepLab v3+](https://www.tensorflow.org/lite/models/segmentation/overview)
+
+* FSRCNN
+
+* RDN converted from [TensorFlow model](https://github.com/hengchuan/RDN-TensorFlow)
+
+* Quantized RDN (CpuRef)
+
+* [Quantized Inception v3](http://download.tensorflow.org/models/tflite_11_05_08/inception_v3_quant.tgz)
+
+* [Quantized Inception v4](http://download.tensorflow.org/models/inception_v4_299_quant_20181026.tgz) (CpuRef)
+
+* Quantized ResNet v2 50 (CpuRef)
+
+* Quantized Yolo v3 (CpuRef)
+
More machine learning operators will be supported in future releases.
* inception_v3. The Arm NN SDK only supports the official inception_v3 transformed model. See the TensorFlow documentation on [preparing models for mobile deployment](https://www.tensorflow.org/mobile/prepare_models) for more information on how to transform the inception_v3 network.
+* ResNet v2 50 implementation from the [TF Slim model zoo](https://github.com/tensorflow/models/tree/master/research/slim)
+
More machine learning operators will be supported in future releases.