Computer Vision Project

These are some mini projects I created using Pytorch for computer vision tasks

in this repo I will create a mini project with various tasks in computer vision such as image classification, object detection, object segmentation, GAN and many more

add a star to get updates from this repo

Repo Update

• 26 Sep 2024 -> inference next js plant-species-classification
• 25 Sep 2024 -> inference next js cat-and-dogs-classification
• 20 Sep 2024 -> facemask-detection
• 17 Sep 2024 -> manga-text-detection
• 17 Sep 2024 -> plant-species-classification
• 16 Sep 2024 -> cat-and-dogs-classification

Dont have CUDA for Training ? Download Model Here

cat-and-dogs-classification (Created : 16-09-2024)

in this project, I focused on fine-tune the resnet-50 model then exported it to onnx format.

which initially resnet-50 outputs approximately 1000 classes into just 2 classes according to the dataset that has been downloaded, namely cats and dogs

• Loss : 0.06402150790199812
• Accuracy : 98.46762234305487%
• Epochs : 5

plant-species-classification (Created : 17-09-2024)

in this project, I focused on fine-tune the google/vit-base-patch16-224 model then exported it to onnx format.

which initially google/vit-base-patch16-224 outputs approximately 1000 classes into just 47 classes according to the dataset that has been downloaded, namely house-plant-species

• Loss : 0.305279920695395
• Accuracy : 93.55418434246046%
• Epochs : 5

manga-text-detection (Created : 17-09-2024)

in this project, I focused on fine-tune the faster-RCNN model then exported it to onnx format.

create object detection on text in manga pages with the manga-text-detection dataset

• Loss : 0.105800
• Epochs : 25
• MAP : 0.9
• MAP-50 : 1.0
• MAP-75 : 0.9901

facemask-detection (Created : 20-09-2024)

in this project, I focused on fine-tune the YOLOv10 model then exported it to onnx format.

create object detection on facemask-dataset

• Loss : 0.405800
• Epochs : 50
• MAP : 0.908
• MAP-50 : 0.934
• MAP-75 : 0.666

Todo List

• Fine tune resnet-50 on the cats and dogs dataset
  • Create inference with opencv and onnxruntime (python)
  • Create inference with next.js and onnxruntime-web
  • Create Inference with kotlin
• Fine tune vit-base-224 on the plant species dataset
  • Create inference with opencv and onnxruntime (python)
  • Create inference with next.js and onnxruntime-web
  • Create Inference with kotlin
• Fine tune Faster-RCNN on the manga text detection dataset
  • Create inference with opencv and onnxruntime (python)
  • Create inference with next.js and onnxruntime-web
• Fine tune YOLO-V10 on the facemask dataset
  • Create inference with opencv and onnxruntime (python)
  • Create inference with next.js and onnxruntime-web
  • Create Inference with kotlin and run with input uint8

Authors

• @naufalahnaf17