Mobilenet cifar 10 accuracy

cifar-10は10クラスの画像分類なので出力ユニット数は10になる。 畳み込み層ではパディングサイズが0だと出力の特徴マップの画像サイズが入力画像より少し小さくなる。 Simplify: Save: Load: Download: Upload: HideBlob: HideNumOutput: Render as SVG: Render as image: Clear board: Remove BN: Add lr=0 Jan 14, 2019 · MobileNet-SSD is a cross-trained model from SSD to MobileNet architecture, which is faster than SSD. In this study, we show a key application area for the SSD and MobileNet-SSD framework. The advantages and shortcomings of the SSD and MobileNet-SSD framework were analyzed using fifty-nine individual traffic cameras. CIFAR-10 examples¶. Overview¶. CIFAR-10 classification is a common benchmark problem in machine learning. The CIFAR-10 dataset is the collection of images. It is one of the most widely used datasets for machine learning research which contains 60,000 32x32 color images in 10 different classes.

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CIFAR‑10 $$\ell_\infty (\epsilon = 8/255)$$ 87% accuracy. 46% accuracy. Provable defenses against adversarial examples via the convex outer adversarial polytope (Wong & Kolter) ICML 2018: MNIST $$\ell_\infty (\epsilon = 0.1)$$ 98.2% accuracy. 94.2% accuracy. Mitigating Adversarial Effects Through Randomization (Xie et al.)

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Faster than Full precision training: If you look at the example of Resnet 101 where the difference is the highest, FP training takes 1.18x time on a 2080Ti and 1.13x time on a 2080Ti for our CIFAR-100 example. A slight speedup is always visible during the training, even for the “smaller” Resnet34 and Resnet50. Cifar-10是由Hinton的两个大弟子Alex Krizhevsky、Ilya Sutskever收集的一个用于普适物体识别的数据集。 因而,Cifar-10相对于传统图像识别数据集,是相当有挑战的。MobileNet/ImageNet inference. 1. Dataset preparation; 2. Create a Keras MobileNet model; 3. Quantized model; 4. Pretrained quantized model; 5. Conversion to Akida; DS-CNN/KWS inference. 1. Load the preprocessed dataset; 2. Load a pre-trained native Keras model; 3. Load a pre-trained quantized Keras model satisfying Akida NSoC requirements; 4 ...


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