regnet-x-006
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From language to images, open weights to APIs. Understand capabilities and compare real costs.
Find the right engine for your next idea.
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RegNet model trained on imagenet-1k. It was introduced in the paper Designing Network Design Spaces and first released in this repository. Disclaimer: The team releasing RegNet did not write a model card for this model so this model card has been written by the Hugging Face team. The authors design search spaces to perform Neural Architecture Search (NAS). They first start from a high dimensional search space and iteratively reduce the search space by empirically applying constraints based on the best-performing models sampled by the current search space.
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Adjust provider, category or input/output filters. New models are added continuously.
Adjust monthly token usage to compare estimated API costs.
Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.
Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.
Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.
Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.