SAVRN Model Hub · Comparisons
efficientnet_b0.ra_in1k vs mobilenetv3_small_100.lamb_in1k
Efficientnet_b0.ra_in1k has 5M parameters and mobilenetv3_small_100.lamb_in1k has 3M parameters; both are released under Apache License 2.0; at 16-bit, efficientnet_b0.ra_in1k needs about 0 GB (1x MI300X from $1.85 an hour) and mobilenetv3_small_100.lamb_in1k about 0 GB (1x MI300X from $1.85 an hour).
| Field | efficientnet_b0.ra_in1k timm/efficientnet_b0.ra_in1k | mobilenetv3_small_100.lamb_in1k timm/mobilenetv3_small_100.lamb_in1k |
|---|---|---|
| Publisher | PyTorch Image Models | PyTorch Image Models |
| Task | Image classification | Image classification |
| Modality | Image | Image |
| Parameters, as reported | 5M parameters | 3M parameters |
| Architecture | Not stated | Not stated |
| Library | timm | timm |
| Context length | Not stated | Not stated |
| Repository size | 42.8 MB | 20.5 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 | 1b5383e5f79c | 1824797e7887 |
| Downloads reported by the hub | 704.1k | 17.7M |
| 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_b0.ra_in1k
Not every entry on this hub needs a GPU budget conversation. The weights total 42.8 MB in 5 files, 5,330,564 parameters, and the job is image classification trained on ImageNet-1k in timm. The memory table reads zero at every precision because the model is small enough for the estimate to round away. The cheapest Index setup, one MI300X with 192 GB at $1.85 per hour, would hold more than four thousand copies of it, so the real question is how many images per second you need, not whether it fits.
Apache License 2.0 covers it: commercial use, modification and redistribution are permitted, and you keep the notices and state significant changes. What to check is age and evidence. It was released December 12, 2022, the recipe traces to arXiv:2110.00476, and no evaluations are reported on file, so run it on your own images first.
SAVRN's Notes on mobilenetv3_small_100.lamb_in1k
Every row of the memory table for this model reads 0.0 GB. The weights total 20,541,585 bytes for 2,554,968 parameters, and the job is image classification over the ImageNet-1k label set. PyTorch Image Models trained it with a LAMB optimizer recipe similar to ResNet Strikes Back A2 but 50 percent longer, with EMA weight averaging and no CutMix. When the footprint rounds to zero at 16-bit, 8-bit and 4-bit, the cheapest configuration we list, a single 192 GB MI300X at $1.85 an hour, is not a sizing answer; the hardware question becomes images per hour and what else shares the card.
Apache 2.0 covers commercial use, modification and redistribution, so a fine-tuned version is yours to ship if you keep the notices and state what changed. Before committing, pull accuracy from timm's model results, since the file carries no reported evaluations, and confirm the ImageNet-1k classes match your images.
Questions
Which is larger, efficientnet_b0.ra_in1k or mobilenetv3_small_100.lamb_in1k?
efficientnet_b0.ra_in1k (5M parameters) is larger than mobilenetv3_small_100.lamb_in1k (3M parameters), by the parameter counts their publishers report.
Which is cheaper to run, efficientnet_b0.ra_in1k or mobilenetv3_small_100.lamb_in1k?
At 4-bit, efficientnet_b0.ra_in1k fits on 1x MI300X from $1.85 an hour and mobilenetv3_small_100.lamb_in1k on 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Can I use efficientnet_b0.ra_in1k commercially?
Yes. efficientnet_b0.ra_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 mobilenetv3_small_100.lamb_in1k commercially?
Yes. mobilenetv3_small_100.lamb_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.