SAVRN Model Hub · Comparisons
efficientnet_b3.ra2_in1k vs repvgg_a0.rvgg_in1k
Efficientnet_b3.ra2_in1k has 12M parameters and repvgg_a0.rvgg_in1k has 9M parameters; efficientnet_b3.ra2_in1k is released under Apache License 2.0 and repvgg_a0.rvgg_in1k under MIT License; at 16-bit, efficientnet_b3.ra2_in1k needs about 0 GB (1x MI300X from $1.85 an hour) and repvgg_a0.rvgg_in1k about 0 GB (1x MI300X from $1.85 an hour).
| Field | efficientnet_b3.ra2_in1k timm/efficientnet_b3.ra2_in1k | repvgg_a0.rvgg_in1k timm/repvgg_a0.rvgg_in1k |
|---|---|---|
| Publisher | PyTorch Image Models | PyTorch Image Models |
| Task | Image classification | Image classification |
| Modality | Image | Image |
| Parameters, as reported | 12M parameters | 9M parameters |
| Architecture | Not stated | Not stated |
| Library | timm | timm |
| Context length | Not stated | Not stated |
| Repository size | 98.8 MB | 73.2 MB |
| Artifact formats | safetensors, pytorch | safetensors, pytorch |
| License | apache-2.0 | mit |
| Access | Open weights, no gate | Open weights, no gate |
| Memory at 16-bit (weights and margin) | 0 GB | 0 GB |
| Cheapest GPUs at 16-bit, per hour | 1x MI300X, $1.85 | 1x MI300X, $1.85 |
| Memory at 4-bit (weights and margin) | 0 GB | 0 GB |
| Cheapest GPUs at 4-bit, per hour | 1x MI300X, $1.85 | 1x MI300X, $1.85 |
| Revision viewed | 0366a7551862 | e292d220aa8b |
| Downloads reported by the hub | 12.7M | 597.3k |
| Last observed | 2026-09-18 | 2026-09-18 |
An evaluation row appears only where at least two of these models report the same benchmark with the same stated configuration, metric, unit and setup. Different evaluators stay named in each cell. Values are shown as reported: no unit conversion, no ranking.
SAVRN's Notes on efficientnet_b3.ra2_in1k
When the memory line reads 0.0 GB at 16-bit, 8-bit and 4-bit alike, the hardware conversation ends before it starts. Twelve million parameters and 98.8 MB on disk across five files is the whole footprint of this image classifier, trained on ImageNet-1k in timm with the RandAugment RA2 recipe. The Index names one MI300X with 192 GB at $1.85 an hour as the cheapest setup, but the classifier will never be what fills that card, so size the node around the rest of the pipeline.
Nothing in the license gets in the way: Apache 2.0 clears commercial use, modification and redistribution with notices kept and changes stated, and adds an express patent grant. Check the lineage instead: ImageNet-1k is the only training set, so confirm its classes cover what you need labeled, and read the recipe in arXiv:2110.00476, which published this as its B recipe.
SAVRN's Notes on repvgg_a0.rvgg_in1k
What does it cost to keep a 9 million parameter image classifier running in your own rack? Close to nothing beyond the accelerator you already own. Weights and working memory both round to 0.0 GB at 16-bit, 8-bit and 4-bit, the five files total about 73 MB, and the cheapest Index listing is a single MI300X at $1.85 per hour on-demand. The sensible deployment is as a resident model on shared hardware or as a feature extractor, since timm exposes per-stage feature extraction.
The license is MIT: commercial use, modification and redistribution are permitted provided the copyright and permission notices stay attached. It was trained on ImageNet-1k, described in arXiv:2101.03697, so any other label set means a fine-tune. And the timm release dates from August 23, 2023 with a last update on January 21, 2025, so confirm you are loading the current weights.
Questions
Which is larger, efficientnet_b3.ra2_in1k or repvgg_a0.rvgg_in1k?
efficientnet_b3.ra2_in1k (12M parameters) is larger than repvgg_a0.rvgg_in1k (9M parameters), by the parameter counts their publishers report.
Which is cheaper to run, efficientnet_b3.ra2_in1k or repvgg_a0.rvgg_in1k?
At 4-bit, efficientnet_b3.ra2_in1k fits on 1x MI300X from $1.85 an hour and repvgg_a0.rvgg_in1k on 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Can I use efficientnet_b3.ra2_in1k commercially?
Yes. efficientnet_b3.ra2_in1k is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.
Can I use repvgg_a0.rvgg_in1k commercially?
Yes. repvgg_a0.rvgg_in1k is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.