12/18/2023 0 Comments Tensorflow swish activation![]() ![]() The consistency of Mish providing better test top-1 accuracy as compared to Swish and ReLU was also observed by increasing Batch Size for a ResNet v2-20 on CIFAR-10 for 50 epochs while keeping all other network parameters to be constant for fair comparison. Increasing number of layers from 15 gradually resulted in a sharp decrease in test accuracy for Swish and ReLU, however, Mish outperformed them both in large networks where optimization becomes difficult. In the experiments, all 3 activations maintained nearly the same test accuracy for 15 layered Network. The network is optimized using SGD on a batch size of 128, and for fair comparison, the same learning rates for each activation function was maintained. BatchNorm was used to lessen the dependence on initialization along with a dropout of 25%. Residual Connections were not used because they enable the training of arbitrarily deep networks. To observe how increasing the number of layers in a network while maintaining other parameters constant affect the test accuracy, fully connected networks of varying depths on MNIST, with each layer having 500 neurons were trained. Variation of Parameter Comparison: MNIST: GPU-175 Watt- FP32/16 (Nvidia GeForce RTX 2070) DarkNet-cuDNN, FPSĬredits to AlexeyAB, Wong Kin-Yiu and Glenn Jocher for all the help with benchmarking MS-COCO and ImageNet. VPU-2 Watt- FP16 (Intel MyriadX) OpenCV-DLIE, FPS ![]() MILA/ CIFAR 2020 DLRLSS (Click on arrow to view)įor Installing DarkNet framework, please refer to darknet(Alexey AB)įor PyTorch based ImageNet scores, please refer to this readme Networkįor PyTorch based MS-COCO scores, please refer to this readme ModelĬPU - 90 Watt - FP32 (Intel Core i7-6700K, 4GHz, 8 logical cores) OpenCV-DLIE, FPS (08/2021) Comprehensive hardware based computation performance benchmark for Mish has been conducted by Benjamin Warner. (12/2020) Weights & Biases integration is now added □. (12/2020): Talk on From Smooth Activations to Robustness to Catastrophic Forgetting at Weights & Biases Salon is out now.(08/2020): Talk on Mish and Non-Linear Dynamics at Computer Vision Talks.(07/2020): CROWN: A comparison of morphology for Mish, Swish and ReLU produced in collaboration with Javier Ideami.(02/2020): Talk on Mish and Non-Linear Dynamics at Sicara is out now.(02/2020): Podcast episode on Mish at Machine Learning Café is out now.Mish added to TFLearn - Merged 1159 (Follow up 1141).Further details on paperswithcode leaderboards. CSP-p7 + Mish (multi-scale) is currently the SOTA in Object Detection on MS-COCO test-dev while CSP-p7 + Mish (single-scale) is currently the 3rd best model in Object detection on MS-COCO test dev.Official paper and presentation video for BMVC is released at this link.Mish added to TensorFlow Swift APIs - Merged - 1068.Mish added to Sony Nnabla - Merged-700.New updated Arxiv version of the paper is out.New updated PyTorch benchmarks and pretrained models available on PyTorch Benchmarks.Mish paper accepted to 31st British Machine Vision Conference (BMVC), 2020.Poster accepted for presentation at DLRLSS hosted by MILA, CIFAR, Vector Institute and AMII. ![]() Loss Landscape exploration progress in collaboration with Javier Ideami and Ajay Uppili Arasanipalai.Mish added to OpenVino - Open-1187, Merged-1125.Variance based initialization method for Mish (experimental) by Federico Andres Lois can be found here - Mish_init.Alternative (experimental improved) variant of H-Mish developed by Páll Haraldsson can be found here - H-Mish (Available in Julia).Faster variants for Mish and H-Mish by Yashas Samaga can be found here - ConvolutionBuildingBlocks.Memory Efficient Experimental version of Mish can be found here.A considerably faster version based on CUDA can be found here - Mish CUDA (All credits to Thomas Brandon for the same).Respected Dr.Yogesh Pawar, who always brings us these programs, deserves a big shoutout for helping us learn and grow. I want to express my heartfelt thanks to the School of Inspirational Leadership team for this opportunity. It was a two-day session in the stunning setting of Mahabaleshwar, which was completely unpredictable and unexpected in terms of knowledge. I'm someone who's always glued to my phone, but this experience taught me the power of mindfulness when we disconnect. It was a special time where I learned some valuable lessons about finding my inner voice and using my energy wisely to achieve my goals. I recently had an amazing experience in Mahabaleshwar during the Burning Desire Program. ![]()
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