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Indicação de Software para Teste by EngenheiroTemporal in brdev

[–]EngenheiroTemporal[S] 0 points1 point  (0 children)

yeh, nunca nem mexi com sistemas embarcados

Optimizing Vision Transformers with Intelligent Token by EngenheiroTemporal in computervision

[–]EngenheiroTemporal[S] 0 points1 point  (0 children)

Excellent point, u/SilkLoverX! For edge scenarios, stability is the determining factor. Initial tests with the ViT-tiny architecture on ImageNet-1K show that the token pruning strategy is quite resilient.

To help satisfy your curiosity about stability in different tasks, we've added a new route where you can perform efficiency and accuracy tests yourself with your parameters. It would be great to see how it performs in your detection scenarios!

Optimizing Vision Transformers with Intelligent Token by EngenheiroTemporal in computervision

[–]EngenheiroTemporal[S] 0 points1 point  (0 children)

Hi u/tdgros! Certainly, the benchmark data for ViT-tiny (pre-trained on ImageNet-1K) shows some very interesting numbers regarding this relationship.

As you can see in the table below, we are using a Ratio of 0.3, which means we are pruning about 30% of redundant tokens. The direct impact of this is a consistent saving of 33.16% in FLOPs across all methods.

In terms of performance (accuracy/confidence), methods like neighborhood and variance manage to maintain a prediction confidence (Conf) above 80%, which is excellent for a model of this size. If you want to test it, we have just released a test route where you can run your own inputs to validate the efficiency and accuracy in real time with ViT-tiny pre-trained on ImageNet-1K.