Captured 85.8M ViT weights
Safetensors metadata reports 85,800,194 parameters, and the hub record lists Transformers support for the image-classification task.
Open Source Model Profile · rizvandwiki
gender-classification-2
gender-classification-2 is an 85.8M-parameter ViT image classifier from rizvandwiki. According to the model card, it was autogenerated by HuggingPics with female and male example labels.
- Publisher
- rizvandwiki
- Task
- image-classification
- Model type
- vit
- License
- Unknown
- Library
- transformers
- Publication status
- Accepted · not indexed
Model overview
gender-classification-2 is published by rizvandwiki as an image-classification model. The captured configuration identifies ViTForImageClassification with model type vit, and Safetensors metadata reports 85,800,194 parameters. According to the model card, it was autogenerated by HuggingPics with female and male example images.
Recorded capabilities
HuggingPics autogenerated origin
According to the model card, this classifier was autogenerated by HuggingPics, with a linked demo workflow for building image classifiers.
Female and male example labels
According to the model card, the documented example images cover female and male labels.
Use cases in the source record
- Binary-style image classification over the publisher-documented female and male labels.
- Template-style classifier experiments built with the publisher-linked HuggingPics demo workflow.
Limitations and unknowns
- No license value was captured for this record, so licensing terms remain unknown.
- No evaluation results were extracted from this record.
- No context-window value applies to this image-classification record beyond the model configuration.
- Provider state is historical snapshot data and should be refreshed before being presented as current.
Source and provenance
Source: rizvandwiki/gender-classification-2
Captured: Unknown. Processed: 2026-09-07T19:34:56.844732+00:00.
gender-classification-2 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 . Example Images female male
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