April 2025
Intermediate to advanced
482 pages
13h 59m
Italian
book
Machine learning pratico per la visione artificiale
by Valliappa Lakshmanan, Martin Görner, Ryan Gillard
Content preview from Machine learning pratico per la visione artificiale
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Capitolo 5. Creare set di dati di visione
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Per eseguire l'apprendimento automatico sulle immagini, abbiamo bisogno di immagini. Tra i casi d'uso esaminati nel Capitolo 4, la maggior parte riguardava l'apprendimento automatico supervisionato. Per questi modelli, abbiamo bisogno anche della risposta corretta, o etichetta, per addestrare il modello ML. Se intendi addestrare un modello di ML non supervisionato o un modello auto-supervisionato, come un Go o un autoencoder, puoi evitare le etichette. In questo capitolo vedremo come creare un dataset di apprendimento automatico composto da immagini ed etichette.
Suggerimento
Il codice di questo capitolo si trova nella cartella 05_create_dataset del repository GitHub del libro. Indicheremo i nomi dei file per gli esempi di codice e per i quaderni di appunti, se necessario.
Raccolta di immagini
Nella maggior parte dei progetti di ML, la prima fase consiste nel raccogliere i dati. La raccolta dei dati può avvenire in diversi modi: montando una telecamera a un incrocio, collegandosi a un catalogo digitale per ottenere fotografie di ricambi auto, acquistando un archivio di immagini satellitari, ecc. Può essere un'attività logistica (montaggio di telecamere per il traffico), tecnica (creazione di un software di collegamento al database del catalogo) o commerciale (acquisto di un archivio di immagini). ...
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