100% accuracy when using a ViT model?
I have used google’s ‘vit-base-patch16-224-in21k’ pretrained ViT model for my image dataset.I have sequences of images (some 1315 images in total, which is not a large dataset), which I am trying to classify as human present, no human present, which is binary classification.Model: “model_6”Layer (type)                 Output Shape              Param #input_7 (InputLayer)         [(None, 3, 224, 224)]     0vit (TFViTMainLayer)         TFBaseModelOutputWithPool 86389248global_average_pooling1d (Gl (None, 768)               0dense_12 (Dense)             (None, 256)               196864dropout_40 (Dropout)         (None, 256)               0outputs (Dense)              (None, 1)                 257Total params: 86,586,369Trainable params: 197,121Non-trainable params: 86,389,248NoneAfter training, I get 100% accuracy on my test, validation and training dataset!my accuracy and loss curves look like this.loss_acc471×609 26.3 KBhow can it be possible?