Week_9.md

# Week-9 Summary

PREVIEW

Total Hours Spent: 21 hours 🟩🟩🟩🟩🟩🟩🟩

Commits: None

Pull Requests: None

Project Status:

• I had started working on YOLOX Inference using openCV's Deep Neural Network framework (cv.dnn). I began testing various models from the YOLOX repo including the legacy models.
• For the project, we made use of the YOLOX_S, YOLOX_tiny, and YOLOX_nano version(s) of YOLOX model since it offers the following advantages over other models.

Version	Resolution	mAPval (0.5:0.95)	FLOPS	Params	Model Size
YOLOX_S	640*640	40.5	9.0	26.8	66.7 MB FP16
YOLOX_Tiny	416*416	25.3	0.91	1.08	40.8 MB FP16
YOLOX_Nano	416*416	32.8	5.06	6.45	7.7 MB FP16

• YOLOX is an anchor-free version of YOLO, with a simpler design but better performance! It aims to bridge the gap between research and industrial communities. We present some experienced improvements to YOLO series, forming a new high-performance detector -- YOLOX. We switch the YOLO detector to an anchor-free manner and conduct other advanced detection techniques, i.e., a decoupled head and the leading label assignment strategy SimOTA to achieve state-of-the-art results across a large scale range of models: For YOLO-Nano with only 0.91M parameters and 1.08G FLOPs, we get 25.3% AP on COCO, surpassing NanoDet by 1.8% AP; for YOLOv3, one of the most widely used detectors in industry, we boost it to 47.3% AP on COCO, outperforming the current best practice by 3.0% AP; for YOLOX-L with roughly the same amount of parameters as YOLOv4-CSP, YOLOv5-L, we achieve 50.0% AP on COCO at a speed of 68.9 FPS on Tesla V100, exceeding YOLOv5-L by 1.8% AP. F

WEEK3 TASKS

• Demonstrated cv.dnn inference of YoloX 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.

YOLOX_S:

Average forward time: 5.53 ms, Average NMS time: 1.71 ms, Average inference time: 7.25 ms

Average Precision	Average Recall
area IoU Average Precision(AP) all 0.50:0.95 0.405 all 0.50 0.593 all 0.75 0.437 small 0.50:0.95 0.232 medium 0.50:0.95 0.448 large 0.50:0.95 0.541	area IoU Average Recall(AR) all 0.50:0.95 0.326 all 0.50:0.95 0.531 all 0.50:0.95 0.574 small 0.50:0.95 0.365 medium 0.50:0.95 0.634 large 0.50:0.95 0.724

YOLOX_tiny:

Average forward time: 2.07 ms, Average NMS time: 1.71 ms, Average inference time: 3.79 ms

Average Precision	Average Recall
area IoU Average Precision(AP) all 0.50:0.95 0.328 all 0.50 0.504 all 0.75 0.346 small 0.50:0.95 0.139 medium 0.50:0.95 0.360 large 0.50:0.95 0.501	area IoU Average Recall(AR) all 0.50:0.95 0.283 all 0.50:0.95 0.450 all 0.50:0.95 0.485 small 0.50:0.95 0.226 medium 0.50:0.95 0.550 large 0.50:0.95 0.687

YOLOX_nano:

Average forward time: 1.68 ms, Average NMS time: 1.64 ms, Average inference time: 3.31 ms

Average Precision	Average Recall
area IoU Average Precision(AP) all 0.50:0.95 0.258 all 0.50 0.414 all 0.75 0.268 small 0.50:0.95 0.082 medium 0.50:0.95 0.275 large 0.50:0.95 0.410	area IoU Average Recall(AR) all 0.50:0.95 0.241 all 0.50:0.95 0.384 all 0.50:0.95 0.420 small 0.50:0.95 0.157 medium 0.50:0.95 0.473 large 0.50:0.95 0.631

• Evaluate model precision metrics on val2017 dataset and report scores for individual class labels. Below are the precision scores observed per class. Below, we have per class AP results on COCO dataset for the models YOLOX_S, YOLOX_tiny, YOLOX_nano respectively

YOLOX_S

class	AP	class	AP	class	AP
person	54.109	bicycle	31.580	car	40.447
motorcycle	43.477	airplane	66.070	bus	64.183
train	64.483	truck	35.110	boat	24.681
traffic light	25.068	fire hydrant	64.382	stop sign	65.333
parking meter	48.439	bench	22.653	bird	33.324
cat	66.394	dog	60.096	horse	58.080
sheep	49.456	cow	53.596	elephant	65.574
bear	70.541	zebra	66.461	giraffe	66.780
backpack	13.095	umbrella	41.614	handbag	12.865
tie	29.453	suitcase	39.089	frisbee	61.712
skis	21.623	snowboard	31.326	sports ball	39.820
kite	41.410	baseball bat	27.311	baseball glove	36.661
skateboard	49.374	surfboard	35.524	tennis racket	45.569
bottle	37.270	wine glass	33.088	cup	39.835
fork	31.620	knife	15.265	spoon	14.918
bowl	43.251	banana	27.904	apple	17.630
sandwich	32.789	orange	29.388	broccoli	23.187
carrot	23.114	hot dog	33.716	pizza	52.541
donut	47.980	cake	36.160	chair	29.707
couch	46.175	potted plant	24.781	bed	44.323
dining table	30.022	toilet	64.237	tv	57.301
laptop	58.362	mouse	57.774	remote	24.271
keyboard	48.020	cell phone	32.376	microwave	57.220
oven	36.168	toaster	28.735	sink	38.159
refrigerator	52.876	book	15.030	clock	48.622
vase	37.013	scissors	26.307	teddy bear	45.676
hair drier	7.255	toothbrush	19.374

YOLOX_tiny

class	AP	class	AP	class	AP
person	45.685	bicycle	22.797	car	29.265
motorcycle	37.980	airplane	59.446	bus	54.878
train	62.459	truck	26.850	boat	16.724
traffic light	17.527	fire hydrant	55.155	stop sign	57.120
parking meter	37.755	bench	17.656	bird	24.382
cat	55.792	dog	50.964	horse	49.806
sheep	39.894	cow	42.855	elephant	58.863
bear	62.345	zebra	58.389	giraffe	62.362
backpack	8.131	umbrella	33.650	handbag	7.777
tie	21.907	suitcase	25.593	frisbee	48.975
skis	16.941	snowboard	19.409	sports ball	30.718
kite	33.956	baseball bat	17.912	baseball glove	28.793
skateboard	38.253	surfboard	28.329	tennis racket	33.240
bottle	23.872	wine glass	20.386	cup	26.962
fork	21.025	knife	8.434	spoon	6.513
bowl	34.706	banana	24.050	apple	12.745
sandwich	28.046	orange	24.216	broccoli	18.579
carrot	16.283	hot dog	30.058	pizza	44.371
donut	35.957	cake	29.765	chair	22.070
couch	41.221	potted plant	19.856	bed	44.173
dining table	29.000	toilet	60.369	tv	49.868
laptop	48.858	mouse	47.843	remote	14.349
keyboard	42.412	cell phone	23.536	microwave	51.839
oven	32.384	toaster	24.209	sink	32.607
refrigerator	50.156	book	9.534	clock	41.661
vase	25.548	scissors	17.612	teddy bear	39.375
hair drier	0.000	toothbrush	9.933

YOLOX_nano

class	AP	class	AP	class	AP
person	38.444	bicycle	16.922	car	21.708
motorcycle	30.753	airplane	47.573	bus	49.651
train	55.302	truck	20.294	boat	11.919
traffic light	12.026	fire hydrant	48.798	stop sign	52.446
parking meter	33.439	bench	13.565	bird	16.520
cat	42.603	dog	43.831	horse	37.338
sheep	27.807	cow	33.155	elephant	52.374
bear	49.737	zebra	52.259	giraffe	56.445
backpack	5.456	umbrella	25.288	handbag	2.802
tie	17.110	suitcase	17.757	frisbee	40.878
skis	13.245	snowboard	11.443	sports ball	22.310
kite	28.107	baseball bat	10.295	baseball glove	20.294
skateboard	28.285	surfboard	19.142	tennis racket	25.253
bottle	15.064	wine glass	13.412	cup	19.357
fork	13.384	knife	4.276	spoon	3.460
bowl	26.615	banana	18.067	apple	9.672
sandwich	22.817	orange	23.574	broccoli	14.710
carrot	10.180	hot dog	18.646	pizza	38.244
donut	24.204	cake	21.330	chair	14.644
couch	33.018	potted plant	13.252	bed	38.034
dining table	24.287	toilet	52.986	tv	44.978
laptop	44.130	mouse	35.173	remote	7.349
keyboard	33.903	cell phone	19.140	microwave	38.800
oven	25.890	toaster	10.665	sink	23.293
refrigerator	42.697	book	6.942	clock	35.254
vase	18.742	scissors	11.866	teddy bear	30.907
hair drier	0.000	toothbrush	7.284

Note

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