SSD MobileNet v1 is a object detection network, that localizes and identifies objects in an input image. This is a TF Lite quantized version that takes a 300x300 input image and outputs detections for this image. This model is converted from FP32 to INT8 using post-training quantization.
Apache-2.0
The class labels associated with this model can be downloaded by running the script get_class_labels.sh.
Code to recreate this model can be found here.
| Platform |
Optimized |
| Cortex-A |
✔️ |
| Cortex-M |
✖️ |
| Mali GPU |
✔️ |
| Ethos U |
✖️ |
- ✔️ - Will run on this platform.
- ✖️ - Will not run on this platform.
Dataset: COCO 2017 Validation
| Optimization |
Value |
| Quantization |
INT8 |
| Input Node Name |
Shape |
Description |
| tfl.quantize |
(1, 300, 300, 3) |
A resized and normalized input image. |
| Output Node Name |
Shape |
Description |
| TFLite_Detection_PostProcess:01 |
() |
The y1, x1, y2, x2 coordinates of the bounding boxes for each detection |
| TFLite_Detection_PostProcess:02 |
() |
The class of each detection |
| TFLite_Detection_PostProcess:03 |
() |
The probability score for each classification |
| TFLite_Detection_PostProcess:04 |
() |
A vector containing a number corresponding to the number of detections |