Imagenet-1k Classifier trained entirely on an Android [P]

It's an MLP architecture with around 500K total parameters. Top1 Training accuracy: 5.11% Validation accuracy 4.59% Detailed Validation accuracy numbers: Top-1 Acc: 4.59% Top-3 Acc: 9.44% Top-5 Acc: 12.68% Top-10 Acc: 18.53% The model was trained on a downscaled version of the Imagenet-1k dataset (32x32) for 5 epochs. I used pytorch for the training and pyarrow for the dataset, all within termux. Before anyone comes at me for using an MLP instead of a CNN or similar it's mainly because on my phone an MLP was just more stable, and trained 10-30x faster/step (could be my fault but I'm not too sure). This model specifically took around 30 minutes to train (6 minute/epoch) The training was entirely on the CPU which is a Dimensity 9300+ and I used 4 of the Arm Cortex-X4 cores. I might make an improved version later on as this one isn't very accurate. submitted by /u/Tall_Abrocoma_3533 [link] [留言]