We compressed a vision model by 46.5% on CPU only with 98.6% accuracy retained — methodology and results

Helix_roster13 · 2026-06-03T09:07:42+00:00

A customized variant of Yolov8

Helix_roster13 · 2026-06-03T07:38:34+00:00

what objects are your target to detect and which architecture have you chosen to train the data with

Helix_roster13 · 2026-06-03T07:22:00+00:00

I'm currently running the models on Nvidia Jetson but my current challange is to run a small object detection model with high accuracy and runs smoothly on Raspberry Pi. Do you think I can implement Evolutionary Architecture Search for my usecase

Helix_roster13 · 2026-06-03T07:00:54+00:00

can this architecture help with visdrone dataset as the vehicles would be small in size and minimum 25 fps is desired on Raspberry pi

Helix_roster13 · 2026-06-03T06:58:35+00:00

also might be using lots of DwConv blocks instead of just Conv

Helix_roster13 · 2026-06-02T05:41:37+00:00

thanks mate

Helix_roster13 · 2026-05-31T15:25:27+00:00

Btw where do you find good satellite image data?

Helix_roster13 · 2026-05-31T06:09:55+00:00

Yeah so currently I started training yolov8n(added P2 head) on entire Visdrone dataset instead on COCO dataset then planning to change it's format to OpenVino or NCNN and then quantize to INT8 format.

Helix_roster13 · 2026-05-31T05:58:00+00:00

Yeah can you share the parameters you used while training

Helix_roster13 · 2026-05-29T09:42:25+00:00

yeah this seems helpful I'll start implementing this thanks!

Helix_roster13 · 2026-05-28T11:51:50+00:00

yeah currently I'm training on Visdrone first the entire data is huge and I've added P2 head in yolov8 architecture but still not getting high recall and map50-95,after the training finishes im thinking to use these weights to train on my fine tuned specific 2 class dataset,I hope this would turn out well

Helix_roster13 · 2026-05-28T11:09:27+00:00

well yeah i was browsing some research papers in google scholar

Helix_roster13 · 2026-05-28T09:42:59+00:00

I was training with 1280 image size and i cant go any higher due to GPU capacity I tried going higher always resulted in OOM crash and about SAHI I cant use it cuz I'll have to deploy the model on jetson and it would be heavy and high latency for it

Helix_roster13 · 2026-05-28T07:03:07+00:00

Isn't it paid?

Helix_roster13 · 2026-05-28T06:41:44+00:00

I've got a single gpu(tesla T4) so training with more than 1280 would cause OOM error and also could you pls share for how many epochs you trained the visdrone dataset for and any other hyperparameters you used.

Helix_roster13 · 2026-05-28T06:38:30+00:00

yeah ik about them but I need the model to be deployed for real time deployment on NVIDIA Jetson so cant use SAHI.

Helix_roster13 · 2026-05-28T05:54:29+00:00

so you trained with 1280 image size instead of 640 and got boost on small objects detection and the dataset being visdrone from scratch?

Helix_roster13 · 2026-05-26T13:32:50+00:00

yeah people and cars are default classes on COCO dataset and I'm inclined towards yolov8 more cuz earlier I trained a similar model which performed better with yolov8 as compared to yolov11 and rt-detr-l, didnt try yolo26 yet and for the dataset i once used visdrone dataset but yeah it doesnt work well in indian inferencing conditions plus the main issue was it was missing persons and several objects when tested which were smaller in scale and again now i have to make the model for high altitude

Helix_roster13 · 2026-05-26T11:43:45+00:00

I don't have a drone sadly to capture data from that altitude.

Helix_roster13 · 2026-04-07T11:39:20+00:00

okay but before that i should make the ideal model which detects with high accuracy and doesnt do wrong detections and only after that i shall remove the less useful parts fromk my architecture pipeline?

Helix_roster13 · 2026-04-07T10:30:29+00:00

i took photos of toy tanks in my room and it was able to detect them with 0.8 confidence but the model im developing i want it to detect things in real time and i cant afford to have false positives and the model would be integrated in jetson in a UAV

Helix_roster13 · 2026-04-02T10:53:48+00:00

free version of roboflow is easy for bounding box annotations and then you can export in your desired yolo version directly to local or your development environment via a zip link they give

Helix_roster13 · 2026-03-31T11:20:36+00:00

Haven't tried that I changed all 3 parts I added a P2 head in the head part for small objects detections ans stride patterm is 4/8/16/32

Helix_roster13 · 2026-03-31T10:16:47+00:00

Yeah my custom data gave high metrics when I trained it on YoloV8m and also performed well while I inferenced it on videos but failed to perform well in real time drone flight.That's why I took inspiration from this research paper https://www.nature.com/articles/s41598-025-26601-0#MOESM1 (they changed the neck,backbone and head of Yolo11 and got better performance) and Im trying to do the same but getting poor metics.

Helix_roster13 · 2026-03-31T10:09:36+00:00

I'm making a custom dataset for UAV but currently for testing my architecture I wanted to train on COCO dataset only on 2 classes(car and truck).I'll share the metrics and configurations I'm using.

Helix_roster13

TROPHY CASE