Autogenerated by HuggingPics Create your own image classifier for anything by running the demo on Google Colab. Report any issues with the demo at the github repo.
Open-weight model · Image classification
nsfw_image_detection
by Falcons.ai Falconsai/nsfw_image_detection
nsfw_image_detection is an open-weight model for image classification from Falcons.ai, released under Apache License 2.0. It has 86M parameters. At 16-bit it needs about 0.2 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index. It draws 1.7M downloads a month.
The Fine-Tuned Vision Transformer (ViT) is a variant of the transformer encoder architecture, similar to BERT, that has been adapted for image classification tasks.
Parameters86M
Context—
Weights1.5 GB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads1.7M
Runs On
What it takes to serve nsfw_image_detection (86M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.
| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
|---|---|---|---|---|---|
| 16-bit | 0.2 GB | 0.2 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.1 GB | 0.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.0 GB | 0.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Oct 13, 2026.
nsfw_image_detection on every accelerator the SAVRN Index prices, at every precision
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.
Model Card
By Falcons.ai, published under apache-2.0, revision 96cb0d0342c7.
Model Card: Fine-Tuned Vision Transformer (ViT) for NSFW Image Classification
Model Description
The Fine-Tuned Vision Transformer (ViT) is a variant of the transformer encoder architecture, similar to BERT, that has been adapted for image classification tasks. This specific model, named "google/vit-base-patch16-224-in21k," is pre-trained on a substantial collection of images in a supervised manner, leveraging the ImageNet-21k dataset. The images in the pre-training dataset are resized to a resolution of 224x224 pixels, making it suitable for a wide range of image recognition tasks.
During the training phase, meticulous attention was given to hyperparameter settings to ensure optimal model performance. The model was fine-tuned with a judiciously chosen batch size of 16. This choice not only balanced computational efficiency but also allowed for the model to effectively process and learn from a diverse array of images.
Configuration
- Architecture
- ViTForImageClassification
- Layers
- 12
- Hidden size
- 768
- Feed-forward size
- 3,072
- Attention heads
- 12
- Stored precision
- float32
- Model type
- vit
Identity and Version
- Repository
- Falconsai/nsfw_image_detection
- Publisher
- Falcons.ai
- Task
- Image classification
- Modality
- Image
- Library
- transformers
- Parameters
- 86M parameters
- Languages
- vit
- Revision
- 96cb0d0342c7afb80cab76ecc58b265fa44da256
- First published
- 2023-10-13
- Last updated
- 2026-09-07
Files and Weights
9 files, 1.5 GB in total. The weights are 4 files totalling 1.5 GB in bin, pt, safetensors.
Weights4 files · 1.5 GB
Configuration3 files · 1.1 KB
Documentation1 file · 10.6 KB
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| falconsai_yolov9_nsfw_model_quantized.pt | Weights | 87.1 MB | ad6659b81050 |
| model.safetensors | Weights | 343.2 MB | 97b2ce64ec14 |
| optimizer.pt | Weights | 686.5 MB | 02ff26c6fe3d |
| pytorch_model.bin | Weights | 343.3 MB | 2a6b06faec56 |
| config.json | Configuration | 724 B | — |
| labels.json | Configuration | 38 B | — |
| preprocessor_config.json | Configuration | 325 B | — |
| README.md | Documentation | 10.6 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
License and Download
- License
- apache-2.0
- Access
- Open weights, no gate
- Download size
- 1.5 GB
Download from Falcons.ai
Released by Falcons.ai through its official repository on Hugging Face. Read the license.
Built From
- Described by arXiv:2010.11929
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 1.5 GB |
| 16-bit | 0.2 GB |
| 8-bit | 0.1 GB |
| 4-bit | 0.0 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Compare nsfw_image_detection
Questions About nsfw_image_detection
How much GPU memory does nsfw_image_detection need?
About 0.2 GB at 16-bit and 0.1 GB at 4-bit: the weights (86M parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run nsfw_image_detection on?
At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 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.
Similar Models
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 2e-05 - trainbatchsize: 8 - evalbatchsize: 8 - lrschedulertype: linear - numepochs: 5.0 - Transformers 4.36.0.dev0 - Pytorch 2.1.1+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0
Autogenerated by HuggingPics Create your own image classifier for anything by running the demo on Google Colab. Report any issues with the demo at the github repo.
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