TI Deep Learning Product User Guide
|
Both Import and inference configuration files for all the below models are part of TIDL software release package
Num | Network Architecture | Source | Comments |
---|---|---|---|
1 | JacintoNet11v2 | Link | |
2 | SqueezeNet 1.1 | Link | |
3 | ResNet 10 | Link | |
4 | MobileNet-1.0 V1 | Link | |
5 | Resnet 50 V1 | Proto Link Model Link | Refer Note 1 |
6 | ShuffleNet v1 | Link | |
7 | VGGNet 16 | Link | Refer Note 2 |
8 | DenseNet121 | Link | |
9 | Resnext50-32x4d | Link | |
10 | JdetNet512x512 | Link | Refer Note 3 |
11 | Pelee - Caffe SSD | Link | Refer Note 3 |
12 | JsegNet21v2 | link | |
13 | ErfNet | link |
Num | Network Architecture | Source | Comments |
---|---|---|---|
1 | MobileNet-1.0 V1 | Frozen Graph Link More models can be found mobilenet_v1.md | Optimize the graph for inference. Refer Note 4 |
2 | InceptionNet v1 | Checkpoint Link | Generate Frozen Graph and Optimize it for inference. Refer Note 5 |
3 | MobileNet-1.0 V2 | Frozen Graph Link More models can be found here | Optimize the graph for inference. Refer Note 4 |
4 | Resnet 50 V1-TF | Checkpoint Link | Generate Frozen Graph and Optimize it for inference. Refer Note 5 |
5 | Resnet 50 V2-TF | Checkpoint Link | Generate Frozen Graph and Optimize it for inference. Refer Note 5 |
6 | ssd_mobilenet_v1_0.75 SSD | Link | Generate Frozen Graph and Optimize it for inference. Refer Note 6 |
7 | ssd_mobilenet_v1 1.0 SSD | Link | Generate Frozen Graph and Optimize it for inference. Refer Note 6 |
8 | ssd_mobilenet_v2 SSD | Link | Generate Frozen Graph and Optimize it for inference. Refer Note 6 |
Num | Network Architecture | Source | Comments |
---|---|---|---|
1 | MobileNet-1.0 V2 | Link | |
2 | SqueezeNet 1.1 | Link | |
3 | Resnet 18 v1 | Link | |
4 | Resnet 18 v2 | Link | |
5 | ShuffleNet v1 | Link | |
6 | VGG 16 | Link | |
7 | Yolo V3 | Link | |
8 | Resnet 34 v1 | Link | |
9 | RegNetx-200mf | Link | Pytorch model from source is saved as onnx model |
10 | RegNetx-400mf | Link | Pytorch model from source is saved as onnx model |
11 | RegNetx-800mf | Link | Pytorch model from source is saved as onnx model |
Num | Network Architecture | Source | Comments |
---|---|---|---|
1 | MobileNet-1.0 V1 | Link | |
2 | MobileNet-1.0 V2 | Link | |
3 | InceptionNet v1 | Link | |
4 | InceptionNet V3 | Link | |
5 | Efficientnet-Lite 0 | Link | |
6 | deeplabv3_mnv2 | Link | |
7 | deeplabv3_mnv2_dm05 | Link | |
8 | mobileNetv1_ssd | Link | |
9 | mobileNetv2_ssd | Link | |
10 | Efficientnet-Lite 0 | Link | |
11 | Efficientnet-Lite 4 | Link |
TI hosts a repository with a collection of example Deep Learning Models for various Computer Vision tasks. These tasks include image classification, segmentation and detection. The models in this repository can be executed either with host simulation mode on X86 PC or on a TI development board. More information on this can be found here
1. Download and Convert the "ResNet_mean.binaryproto" to simple raw float file Modify Below layer in ResNet-50-deploy.prototxt, replace kernel_size: 7 with global_pooling: true
layer { bottom: "res5c" top: "pool5" name: "pool5" type: "Pooling" pooling_param { kernel_size: 7 stride: 1 pool: AVE } }
2.
3.
4.
5.
6.
Comment or remove the below line in the pipeline.config file if any error observed while export_inference_graph step.
Commands used are :