This occurs when the model incorrectly classifies healthy individuals as having the disease. While this leads to patient anxiety, unnecessary follow-up tests, and increased costs, it is generally considered less harmful than failing to detect an actual disease case (a false negative).

For critical applications like early cancer detection, low sensitivity is unacceptable, as missing real cases can lead to delayed treatment and potentially fatal outcomes.

On the other hand, low precision is characterized by a high number of false positives (FP).

'Of all the people with the disease, what fraction did the model correctly detect?' High sensitivity corresponds to a low number of false negatives (FN), meaning the model is less likely to miss a diagnosis.

At first, all entries of this matrix are set to zero. Then, each prediction is compared with its true label, and their corresponding entry is incremented by one. Confusion matrix entries are used to calculate counting metrics like TP, FP, FN, and TN.

github.com/GoktugGuverc...

It shows how many times the model predicted each class compared to ground truth labels. While the rows represent the predictions, the columns refer to ground-truth labels. Each row and column are defined to denote a specific class index.

Beni de ekleyebilirsiniz :)

Ben de benzer bir karar aldım. Orada daha fazla community olmasına, ve oranın daha çok kullanılmasına rağmen dediğin üzere uyguladığı son politikalarla iyice X’ten bir soğuma geldi ve hani ders de almıyor o kadar şikayet edilmesine rağmen.

Heyy, hoşgeldin

In that case, embedding vectors generated for different images
become constant. This is called dimensional collapse.

In the general concept of this process, random transformation techniques are applied to the images to come up with positive matching pairs of similar images. At that point, the representations and features extracted by the network may collapse into one single point.

Compared with CNNs, transformers are
computationally more expensive and require much more data for successful training. Besides, transformer features do not tend to exhibit distinct properties.

Hafiften buraya geçmeye başladım, twitterdaki şu kısıtlamalar kabak tadı vermeye başladı

Asıl sıkıntılı olan durum bence şu. Takip edilen birçok bilimsel içerik ve account burada yok. Misal, google-health’in publish ettiği makalelere bakıyorum twitter’da ama bunu burada yapmam mümkün değil. Hala invitation olayı, bi tık sıkıntı