SAVRN Model Hub · Comparisons
mobilenetv3_small_100.lamb_in1k vs mobilenetv4_conv_small.e2400_r224_in1k
Mobilenetv3_small_100.lamb_in1k has 3M parameters and mobilenetv4_conv_small.e2400_r224_in1k has 4M parameters; both are released under Apache License 2.0; at 16-bit, mobilenetv3_small_100.lamb_in1k needs about 0 GB (1x MI300X from $1.85 an hour) and mobilenetv4_conv_small.e2400_r224_in1k about 0 GB (1x MI300X from $1.85 an hour).
| Field | mobilenetv3_small_100.lamb_in1k timm/mobilenetv3_small_100.lamb_in1k | mobilenetv4_conv_small.e2400_r224_in1k timm/mobilenetv4_conv_small.e2400_r224_in1k |
|---|---|---|
| Publisher | PyTorch Image Models | PyTorch Image Models |
| Task | Image classification | Image classification |
| Modality | Image | Image |
| Parameters, as reported | 3M parameters | 4M parameters |
| Architecture | Not stated | Not stated |
| Library | timm | timm |
| Context length | Not stated | Not stated |
| Repository size | 20.5 MB | 30.5 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 | 1824797e7887 | 331fb8037795 |
| Downloads reported by the hub | 17.7M | 112.7k |
| Last observed | 2026-09-19 | 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 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, mobilenetv3_small_100.lamb_in1k or mobilenetv4_conv_small.e2400_r224_in1k?
mobilenetv4_conv_small.e2400_r224_in1k (4M parameters) is larger than mobilenetv3_small_100.lamb_in1k (3M parameters), by the parameter counts their publishers report.
Which is cheaper to run, mobilenetv3_small_100.lamb_in1k or mobilenetv4_conv_small.e2400_r224_in1k?
At 4-bit, mobilenetv3_small_100.lamb_in1k fits on 1x MI300X from $1.85 an hour and mobilenetv4_conv_small.e2400_r224_in1k on 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
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.
Can I use mobilenetv4_conv_small.e2400_r224_in1k commercially?
Yes. mobilenetv4_conv_small.e2400_r224_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.