Λநग़͢Δ ྫɿ.3*ͳͲͷղ૾ͷ্ ✦ ϏοάσʔλͷରԠ ★ ύλʔϯೝࣝʢྫɿػցֶशʣʹΑΔݕ ग़ɾൃݟͷޮԽ ★ ෳࡶͳλεΫҙࢥܾఆͷࣗಈԽ ʢΦʔτϝʔγϣϯʣ IUUQTQBSTFNPEFMJOHKQ Figure 6: Qualitative Results. YOLO running on sample artwork and natural images from the internet. It is mostly accurate although it does think one person is an airplane. including the time to fetch images from the camera and dis- play the detections. The resulting system is interactive and engaging. While YOLO processes images individually, when attached to a webcam it functions like a tracking system, detecting ob- jects as they move around and change in appearance. A demo of the system and the source code can be found on directly on full images. Unlike classifier-based approaches, YOLO is trained on a loss function that directly corresponds to detection performance and the entire model is trained jointly. Fast YOLO is the fastest general-purpose object detec- tor in the literature and YOLO pushes the state-of-the-art in real-time object detection. YOLO also generalizes well to +3FENPOFUBM
the PPD T Tau system. The same color scale given by a power law with a scaling exponent of except for the CLEAN model image (γ = 0.3). A white bar of 0 1 (=14.4 au) is provided for reference to the angular scales. (a) SpM image. e denotes the effective spatial resolution with a size of 0 038 × 0 027 for a PA of 45° .3 in the lower left corner. The resolution is estimated from ce simulation. The contour corresponds to IDT , where IDT is the detection threshold of 272 mJy asec 2 - . Note that the SpM image is not processed m convolution as a CLEAN image is, and the unit of the SpM image is not Jy beam−1. The unit of the SpM image that was initially obtained from l−1, and we convert it to Jy arcsec 2 - . (b) Close-up view centered on T Tau N of the SpM image. A field of view of 0 5 × 0 5 is adopted. (c) BVʢఱจ୯Ґʣ .:BNBHVDIJFUBM BVʢఱจ୯Ґʣ BVʢఱจ୯Ґʣ ݱࡏओྲྀͷը૾෮ݩख๏ εύʔεϞσϦϯάʢ৽ख๏ʣ
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