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Jonas edited this page Nov 22, 2023
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A list of image / video datasets used to train a military vehicle detection algorithm
- The Search 2 dataset consists of 44 high-resolution digital color images of different complex natural scenes. Each scene (image) contains a single military vehicle that serves as a search target.
- Sensor Data Management System (SDMS). The video sample sets #1 and #2 contain video clips from the DARPA VIVID Data Collection 1 collected at Eglin, AFB December 2nd and 3rd 2003. The clips are taken from three sensors: EO Daylight TV(DLTV), EO DLTV Spotter, and IR. The scenes contain military and civilian vehicles in many settings. All clips are approximately 60 seconds each and are in AVI format.
- Military vehicles datasets from Kaggle:
- Open Images V7. Open Images is a dataset of ~9M images annotated with image-level labels, object bounding boxes, object segmentation masks, visual relationships, and localized narratives. It contains a total of 16M bounding boxes for 600 object classes on 1.9M images.
- ImageNet. ImageNet, also known as ILSVRC 2012, is an image dataset organized according to the WordNet hierarchy. Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a “synonym set” or “synset”. There are more than 100,000 synsets in WordNet, majority of them are nouns (80,000+). ImageNet provides on average 1,000 images to illustrate each synset.
- Army Recognition Database of world army equipment and recognition information.
- Identify Military Vehicles in Satellite Imagery with TensorFlow
- Military Vehicles Tracking
- Automated Military Vehicle Detection From Low-Altitude Aerial Images
- Military Vehicles CNN
- Fine-tune YOLOv8 models for custom use cases with the help of FiftyOne
- Vehicle or Non-Vehicle
- Vehicle: Military or Non-Military
- Vehicle - Military:
- MILITARY ARMOURED
- HEAVY EXPANDED MOBILITY TACTICAL TRUCK (HEMTT)
- MILITARY TRUCK
- HIGH MOBILITY MULTI-PURPOSE WHEELED VEHICLE
- MILITARY CAR
- MILITARY AMBULANCE
- Self-propelled artillery
- Self-propelled anti-aircraft
- Amphibious armored scout car
- Armored personnel carrier
- Military cargo truck
- Main battle tank
- PyTorch transform package: https://pytorch.org/vision/stable/transforms.html . Specifically, RandomZoomOut and ScaleJitter
- Yolov8 already applies random data augmentation when training : https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/augment.py (see https://github.com/ultralytics/ultralytics/issues/2701 and https://github.com/ultralytics/yolov5/issues/7572 on how to configure / manage data augmentations with yolo training)
- Albumentations lib