This is the Linux app named FastViT whose latest release can be downloaded as ml-fastvitsourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.
Download and run online this app named FastViT with OnWorks for free.
Urmați aceste instrucțiuni pentru a rula această aplicație:
- 1. Ați descărcat această aplicație pe computer.
- 2. Introduceți în managerul nostru de fișiere https://www.onworks.net/myfiles.php?username=XXXXX cu numele de utilizator pe care îl doriți.
- 3. Încărcați această aplicație într-un astfel de manager de fișiere.
- 4. Porniți emulatorul online OnWorks Linux sau Windows online sau emulatorul online MACOS de pe acest site web.
- 5. Din sistemul de operare OnWorks Linux pe care tocmai l-ați pornit, accesați managerul nostru de fișiere https://www.onworks.net/myfiles.php?username=XXXXX cu numele de utilizator dorit.
- 6. Descărcați aplicația, instalați-o și rulați-o.
SCREENSHOTS
Ad
FastViT
DESCRIERE
FastViT is an efficient vision backbone family that blends convolutional inductive biases with transformer capacity to deliver strong accuracy at mobile and real-time inference budgets. Its design pursues a favorable latency-accuracy Pareto curve, targeting edge devices and server scenarios where throughput and tail latency matter. The models use lightweight attention and carefully engineered blocks to minimize token mixing costs while preserving representation power. Training and inference recipes highlight straightforward integration into common vision tasks such as classification, detection, and segmentation. The codebase provides reference implementations and checkpoints that make it easy to evaluate or fine-tune on downstream datasets. In practice, FastViT offers drop-in backbones that reduce compute and memory pressure without exotic training tricks.
Categorii
- Hybrid Conv-Transformer blocks optimized for latency
- Competitive accuracy at mobile/edge inference budgets
- Reference training scripts and pretrained checkpoints
- Compatibility with standard detection/segmentation heads
- Memory-efficient attention and token mixing components
- Simple integration into existing PyTorch pipelines
Limbaj de programare
Piton
Categorii
This is an application that can also be fetched from https://sourceforge.net/projects/fastvit.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.
