This is the Linux app named VGGFace2 whose latest release can be downloaded as vgg_face2sourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.
Download and run online this app named VGGFace2 with OnWorks for free.
Segui queste istruzioni per eseguire questa app:
- 1. Scaricata questa applicazione sul tuo PC.
- 2. Entra nel nostro file manager https://www.onworks.net/myfiles.php?username=XXXXX con il nome utente che desideri.
- 3. Carica questa applicazione in tale file manager.
- 4. Avviare l'emulatore online OnWorks Linux o Windows online o l'emulatore online MACOS da questo sito Web.
- 5. Dal sistema operativo OnWorks Linux che hai appena avviato, vai al nostro file manager https://www.onworks.net/myfiles.php?username=XXXXX con il nome utente che desideri.
- 6. Scarica l'applicazione, installala ed eseguila.
IMMAGINI
Ad
VGGFace2
DESCRIZIONE
VGGFace2 is a large-scale face recognition dataset developed to support research on facial recognition across variations in pose, age, illumination, and identity. It consists of 3.31 million images covering 9,131 subjects, with an average of over 360 images per subject. The dataset was collected from Google Image Search, ensuring a wide diversity in ethnicity, profession, and real-world conditions. It is split into a training set with 8,631 identities and a test set with 500 identities, making it suitable for benchmarking and large-scale model training. Alongside the dataset, the repository provides pre-trained models based on ResNet-50 and SE-ResNet-50 architectures, trained with both MS-Celeb-1M pretraining and fine-tuning on VGGFace2. These models achieve strong verification performance on benchmarks such as IJB-B and include variants with lower-dimensional embeddings for compact feature representation. The project also includes preprocessing tools, face detection scripts, and etc.
Caratteristiche
- Dataset of 3.31 million images across 9,131 subjects
- Training set (8,631 identities) and test set (500 identities)
- Covers wide variations in age, pose, illumination, and ethnicity
- Pre-trained models in ResNet-50 and SE-ResNet-50 architectures
- Models available in Caffe, MatConvNet, PyTorch, and Keras
- Includes preprocessing scripts with MTCNN and evaluation examples
Linguaggio di programmazione
MATLAB, Pitone
Categorie
This is an application that can also be fetched from https://sourceforge.net/projects/vggface2.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.
