Fix classification example for tensorflow models - Copy original image to show image before pre-processing, because pre-processing will change BGR to RGB for tensorflow models - Subtract 1 from output object class index, because tensorflow outputs 1001 bytes and uses index-0 for background. Regular imagenet labels only have 1000 entries. - Fix path to inceptionnet net and params binaries in the config file. - MCT-1221
PLSDK-2986: update TIDL models for mobilenetV1, inceptionNetV1, squeezeNetV1.
Update network binary in TIDL-API to new format - New network format corresponds to the network data structure update, where strideOffsetMethod field moved from sTIDL_Network_t to sTIDL_Layer_t. Old format is 483364 bytes, new format is 484384 bytes. - Relates to: commit 49401e64374a4f0999479245dcd01eab38bec304, MCT-1136 - MCT-1203
PLSDK-2597 - SSD_Multibox: updated to include slider for run-time probability modification - SSD_Multibox: skip grabbing frame input multiple times, as real-time would very based on multicore configuration and network complexity - SSD_Multibox: resize and central cropping added; instead of showing rectangles in original image, network input is presented - Classification: Toydogs configuration added including models Signed-off-by: Djordje Senicic <x0157990@ti.com>
Enable MNIST example on DSP - It turns out DSP implementation of InnerProduct layer in TIDL library requires input size to be multiple of 8, because it is doing aligned 8-byte loads. - Original LeNet network used in the MNIST example has a second InnerProduct layer of size 500, which is not a multiple of 8. Change the size to 504, re-train the network, re-import into TIDL format. Now the MNIST example works correctly on DSP as well. - MCT-1105
Add jdetnet_voc network and make it the default - jdetnet_voc is trained with more object categories than original jdetnet. Make jdetnet_voc the default in the ssd example. User can still use command line options to run the original jdetnet network. - MCT-1091
tidl_models: Add mobilenet and inceptionnet models, trained on ImageNet Signed-off-by: Djordje Senicic <x0157990@ti.com>
mcbench: Multicore benchmark with minimal overhead - Add required models, input test vectors and platform specific scripts - Add inference configuration files for multicore benchmarking - Rename input files to indicate multiple frames and add more inference configurations, covered in regression scripts (MCT-1075)
Add ssd_multibox example to tinn (Part 1) - Uses Single Shot Multibox Detector network in the example - Implementation in part 1 runs a full network on a single device - Add a separate object_class_table for labels and colors - MCT-974
Initial commit (MCT-958)