Week_3.md

# Week-3 Summary

PREVIEW

Total Hours Spent: 18 hours 🟩🟩🟩🟩🟩🟩

Commits: None

Pull Requests: None

Project Status:

• I had started working on Nanodet Inference using openCV's Deep Neural Network framework (cv.dnn). I began testing various models from the Nanodet repo including the legacy models.
• For the project, we made use of the NanoDet-Plus-m-1.5x version of Nanodet model since it offers the following advantages over other models.

Resolution	mAPval (0.5:0.95)	CPU Latency (i7-8700)	ARM Latency (4xA76)	FLOPS	Params	Model Size
416*416	34.1	11.50ms	25.49ms	2.97G	2.44M	4.7MB(FP16) & 2.3MB(INT8)

• In NanoDet-Plus, we propose a novel label assignment strategy with a simple assign guidance module (AGM) and a dynamic soft label assigner (DSLA) to solve the optimal label assignment problem in lightweight model training. We also introduce a light feature pyramid called Ghost-PAN to enhance multi-layer feature fusion. These improvements boost previous NanoDet's detection accuracy by 7 mAP on COCO dataset.

WEEK3 TASKS

• Demonstrated cv.dnn inference of nanodet on test images from val2017 COCO dataset.work
• Run benchmark of the model on COCO val2017 dataset and report the scores for Average Precision (AP) and Average Recall (AR), the results observed are shared below.

Average Precision	Average Recall
area IoU Average Precision(AP) all 0.50:0.95 0.304 all 0.50 0.459 all 0.75 0.317 small 0.50:0.95 0.107 medium 0.50:0.95 0.322 large 0.50:0.95 0.478	area IoU Average Recall all 0.50:0.95 0.278 all 0.50:0.95 0.434 all 0.50:0.95 0.462 small 0.50:0.95 0.198 medium 0.50:0.95 0.510 large 0.50:0.95 0.702

• Evaluate model precision metrics on val2017 dataset and report scores for individual class labels. Below are the precision scores observed per class.

class	AP50	mAP	class	AP50	mAP
person	67.5	41.8	bicycle	35.4	18.8
car	45.0	25.4	motorcycle	58.9	33.1
airplane	77.3	58.9	bus	68.8	56.4
train	81.1	60.5	truck	38.6	24.7
boat	35.5	16.7	traffic light	30.5	14.0
fire hydrant	69.8	54.5	stop sign	60.9	54.6
parking meter	55.1	38.5	bench	26.8	15.9
bird	38.3	23.6	cat	82.5	62.1
dog	67.0	51.4	horse	64.3	44.2
sheep	57.7	35.8	cow	61.2	39.9
elephant	79.9	56.2	bear	81.8	63.0
zebra	85.4	59.5	giraffe	84.1	59.9
backpack	12.4	5.9	umbrella	46.5	28.8
handbag	8.4	3.7	tie	35.2	19.6
suitcase	38.1	23.8	frisbee	60.7	43.9
skis	30.5	14.5	snowboard	32.3	18.2
sports ball	37.6	24.5	kite	51.1	30.4
baseball bat	28.9	13.6	baseball glove	40.1	21.6
skateboard	59.4	35.2	surfboard	47.9	26.6
tennis racket	55.2	30.5	bottle	34.7	20.2
wine glass	27.8	16.3	cup	35.5	23.7
fork	25.9	14.8	knife	10.9	5.6
spoon	8.7	4.1	bowl	42.8	29.4
banana	35.5	18.5	apple	19.4	12.9
sandwich	46.7	33.4	orange	35.2	25.9
broccoli	36.4	19.1	carrot	30.9	17.8
hot dog	42.7	29.3	pizza	61.0	44.9
donut	47.3	34.0	cake	39.9	24.4
chair	28.8	16.1	couch	60.5	42.6
potted plant	29.0	15.3	bed	63.3	46.0
dining table	39.6	27.5	toilet	71.3	55.3
tv	66.5	48.1	laptop	62.6	46.9
mouse	63.5	44.1	remote	19.8	10.3
keyboard	62.1	41.5	cell phone	33.7	22.8
microwave	54.9	39.6	oven	48.1	30.4
toaster	30.0	16.4	sink	44.5	27.8
refrigerator	63.2	46.1	book	18.4	7.3
clock	57.8	35.8	vase	33.7	22.1
scissors	27.8	17.8	teddy bear	54.1	35.4
hair drier	2.9	1.1	toothbrush	13.1	8.2

Note

🟩 - 3 hours of coding (working days: Monday - Saturday)