SAVRN Model Hub · Comparisons
nsfw_image_detection vs rorshark-vit-base
Nsfw_image_detection has 86M parameters and rorshark-vit-base has 86M parameters; both are released under Apache License 2.0; at 16-bit, nsfw_image_detection needs about 0.2 GB (1x MI300X from $1.85 an hour) and rorshark-vit-base about 0.2 GB (1x MI300X from $1.85 an hour).
| Field | nsfw_image_detection Falconsai/nsfw_image_detection | rorshark-vit-base amunchet/rorshark-vit-base |
|---|---|---|
| Publisher | Falcons.ai | Chester Enright |
| Task | Image classification | Image classification |
| Modality | Image | Image |
| Parameters, as reported | 86M parameters | 86M parameters |
| Architecture | ViTForImageClassification | ViTForImageClassification |
| Library | transformers | transformers |
| Context length | Not stated | Not stated |
| Repository size | 1.5 GB | 343.3 MB |
| Artifact formats | safetensors, pytorch | safetensors, tensorboard |
| License | apache-2.0 | apache-2.0 |
| Access | Open weights, no gate | Open weights, no gate |
| Memory at 16-bit (weights and margin) | 0.2 GB | 0.2 GB |
| Cheapest GPUs at 16-bit, per hour | 1x MI300X, $1.85 | 1x MI300X, $1.85 |
| Memory at 4-bit (weights and margin) | 0.1 GB | 0.1 GB |
| Cheapest GPUs at 4-bit, per hour | 1x MI300X, $1.85 | 1x MI300X, $1.85 |
| Revision viewed | 96cb0d0342c7 | 85b973ba04fa |
| Downloads reported by the hub | 1.7M | 788.9k |
| Last observed | 2026-10-16 | 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.
Other Reported Results
These results are listed for each model on its own, because the conditions needed to compare them are not stated or do not match. Two results that leave a condition blank are not assumed to share it.
rorshark-vit-base
| Benchmark | Conditions | Result | Reported by | Revision | Date |
|---|---|---|---|---|---|
| imagefolder | Configuration defaultTask Image ClassificationMetric AccuracyComparison conditions not established | 0.992293 | amunchet Publisher reported |
Evaluated revision not stated | — |
SAVRN's Notes on nsfw_image_detection
An 86M-parameter ViT fine-tune — the card names google/vit-base-patch16-224-in21k, ImageNet-21k pretraining, 224×224 input — for NSFW image screening. The repo carries 1.5 GB of float32 in bin, pt and safetensors copies; at 16-bit the model needs 0.2 GB and at 4-bit effectively nothing. The $1.85-an-hour MI300X in the Runs On table is a formality: this belongs on a CPU next to your upload pipeline, and 2.2M monthly downloads suggest exactly that use.
Apache 2.0 permits commercial moderation use with notices kept, and no host on our Index prices it per token. Treat it as a first-pass filter, not a decision-maker: single-label ViT classifiers age fast, and while the repo was updated 2026-09-07, the architecture is a 2020 design. Calibrate the threshold on your own data before trusting the default operating point — the card describes the model, not its precision and recall.
SAVRN's Notes on rorshark-vit-base
This is a ViT base model fine-tuned on a private image folder dataset, and the card does not say what the classes are. It still draws about 789,000 downloads a month, most likely from automated pipelines rather than people choosing it.
Apache 2.0 allows commercial use. Without published labels, training data or intended use, we cannot recommend it for anything you need to explain. Inspect the label map in its configuration before using it, and prefer models with documented training data.
Questions
Which is larger, nsfw_image_detection or rorshark-vit-base?
nsfw_image_detection (86M parameters) is larger than rorshark-vit-base (86M parameters), by the parameter counts their publishers report.
Which is cheaper to run, nsfw_image_detection or rorshark-vit-base?
At 4-bit, nsfw_image_detection fits on 1x MI300X from $1.85 an hour and rorshark-vit-base on 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Can I use nsfw_image_detection commercially?
Yes. nsfw_image_detection 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 rorshark-vit-base commercially?
Yes. rorshark-vit-base 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.