SAVRN Model Hub · Comparisons
efficientnet_b3.ra2_in1k vs resnet18.a1_in1k
Efficientnet_b3.ra2_in1k has 12M parameters and resnet18.a1_in1k has 12M parameters; both are released under Apache License 2.0; at 16-bit, efficientnet_b3.ra2_in1k needs about 0 GB (1x MI300X from $1.85 an hour) and resnet18.a1_in1k about 0 GB (1x MI300X from $1.85 an hour).
| Field | efficientnet_b3.ra2_in1k timm/efficientnet_b3.ra2_in1k | resnet18.a1_in1k timm/resnet18.a1_in1k |
|---|---|---|
| Publisher | PyTorch Image Models | PyTorch Image Models |
| Task | Image classification | Image classification |
| Modality | Image | Image |
| Parameters, as reported | 12M parameters | 12M parameters |
| Architecture | Not stated | Not stated |
| Library | timm | timm |
| Context length | Not stated | Not stated |
| Repository size | 98.8 MB | 93.7 MB |
| Artifact formats | safetensors, pytorch | safetensors, pytorch |
| License | apache-2.0 | apache-2.0 |
| 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 | 491b427b45c9 |
| Downloads reported by the hub | 12.7M | 2M |
| 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 resnet18.a1_in1k
Ninety-four megabytes on disk, 12M parameters, and a memory line that rounds to 0.0 GB at 16-bit, 8-bit and 4-bit alike, so fit is not the question. The lowest hourly rate in the Index, $1.85 for one MI300X with 192 GB, is a price floor, not a sizing answer; what you size is throughput, images labeled per hour and copies per card. Two million downloads a month against 15 likes: plumbing, not a showpiece.
Apache 2.0 covers it: commercial use, modification and redistribution are allowed if you keep the license and copyright notices and any NOTICE file and state significant changes, with an express patent grant from contributors. The check before committing is training data: ImageNet-1k, under the ResNet Strikes Back A1 recipe, so the labels it emits are ImageNet's, not yours. Two papers, arXiv:1512.03385 and arXiv:2110.00476, describe the architecture and recipe.
Questions
Which is larger, efficientnet_b3.ra2_in1k or resnet18.a1_in1k?
efficientnet_b3.ra2_in1k (12M parameters) is larger than resnet18.a1_in1k (12M parameters), by the parameter counts their publishers report.
Which is cheaper to run, efficientnet_b3.ra2_in1k or resnet18.a1_in1k?
At 4-bit, efficientnet_b3.ra2_in1k fits on 1x MI300X from $1.85 an hour and resnet18.a1_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 resnet18.a1_in1k commercially?
Yes. resnet18.a1_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.