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@@ -588,7 +588,7 @@ When IPL (Initial Program Loader) in ROM starts to run on SCPU after power-on or
Both SCPU and NCPU firmware run RTOS with SCPU handling application, media input/output and peripheral drivers and NCPU handling CNN model pre/post processing. Two CPUs use interrupts and shared memory to achieve IPC (Inter Processor Communication).
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P_n&space; "AP = \sum_n (R_n-R_{n-1})P_n")
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-where 
and 
are the precision and recall at the nth threshold. The mAP compares the ground-truth bounding box to the detected box and returns a score. The higher the score, the more accurate the model is in its detections.
+where and are the precision and recall at the nth threshold. The mAP compares the ground-truth bounding box to the detected box and returns a score. The higher the score, the more accurate the model is in its detections.
## Evaluation on a Dataset
For evaluating the trained model on dataset:
diff --git a/docs/model_training/object_detection_yolov5.md b/docs/model_training/object_detection_yolov5.md
index c2fde03..9090d79 100644
--- a/docs/model_training/object_detection_yolov5.md
+++ b/docs/model_training/object_detection_yolov5.md
@@ -41,12 +41,12 @@ After using a tool like [CVAT](https://github.com/openvinotoolkit/cvat), [makese
- Class numbers are zero-indexed (start from 0).
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Memory layouts
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Figure 4. Pre-edited model
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Figure 1. Summary for platform 520, mode 0 (ip evaluator only)
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Figure 2. Summary for platform 530, mode 0 (ip evaluator only)
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Figure 3. Summary for platform 520, mode 1 (with fix model generated)
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Figure 4. Summary for platform 730, model 2 (with fix model generated and snr check.)
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Figure 5. Node details for platform 520, mode 0 (ip evaluator only).
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Figure 6. Node details for platform 530, mode 0 (ip evaluator only).
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Figure 7. Node details for platform 520, mode 1 (with fix model generated).
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Figure 8. NOde details for platform 730, mode 2 (with fix model generated and snr check).
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Notes:
diff --git a/docs/toolchain/appendix/yolo_example.md b/docs/toolchain/appendix/yolo_example.md
index 5f6f40d..cedbf97 100644
--- a/docs/toolchain/appendix/yolo_example.md
+++ b/docs/toolchain/appendix/yolo_example.md
@@ -95,7 +95,7 @@ Now, we go through all toolchain flow by KTC (Kneron Toolchain) using the Python
* Run "python" or 'ipython'to open to Python shell:
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Figure 1. python shell
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Figure 2. modify input image in example
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Figure 3. modify normalization method in example
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Figure 4. detection result
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Figure 1. python shell
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Figure 1. Diagram of working flow
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Figure FAQ3.1 VirtualBox
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Figure FAQ3.2 VM status
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Figure FAQ3.3 VM shutdown
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Figure FAQ3.4 VM settings
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Figure 1. PTQ Chart
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Figure 1. SNR-FPS Chart
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Figure 2. Sentivity Analysis
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