TI Deep Learning Library User Guide
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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 |
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 } }
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Comment or remove the below line in the pipeline.config file if any error observed while export_inference_graph step.
Commands used are :