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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed]

Image classification
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lab1-cifar10

petitoff

Submission branch: `s_109197_103216`. Fine-tuned ImageNet ResNet-18 in Google Colab; only layer4 and fc are updated. The learning rate and epoch are selected by validation accuracy. The test split is used only after model selection. Seed: 42. Samples: train=10000, validation=2000, test=2000. Validation accuracy: 0.8870; test accuracy: 0.8795. Classes: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck. This model recognizes these ten classes and does not detect out-of-distribution images. CIFAR-10 consists of small 32x32 images, so predictions on arbitrary photographs may be unreliable.

Image classification
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Tag your anime images with **30,877 tags** for characters, clothing, styles, and more. Use the tags to organize your collection, prepare a dataset, or help write captions. **Cutoff: May 2026.** You’ll need PyTorch, torchvision, Transformers, timm, NumPy, and Pillow. Then load the tagger and pass it an image:

Image classification
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> **Age-Classification-SigLIP2** is an image classification vision-language encoder model fine-tuned from **google/siglip2-base-patch16-224** for a single-label classification task. It is designed to predict the age group of a person from an image using the **SiglipForImageClassification** architecture. The model categorizes images into five age groups: - **Class 0:** "Child 0-12" - **Class 1:** "Teenager 13-20" - **Class 2:** "Adult 21-44" - **Class 3:** "Middle Age 45-64" - **Class 4:** "Aged 65+" The **Age-Classification-SigLIP2** model is designed to classify images into five age categories. Potential use cases include:

Image classification
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**AI-vs-Deepfake-vs-Real-Siglip2** is an image classification vision-language encoder model fine-tuned from google/siglip2-base-patch16-224 for a single-label classification task. It is designed to distinguish AI-generated images, deepfake images, and real images using the SiglipForImageClassification architecture. The model categorizes images into three classes: - **Class 0:** "AI" – The image is fully AI-generated, created by machine learning models. - **Class 1:** "Deepfake" – The image is a manipulated deepfake, where real content has been altered. - **Class 2:** "Real" – The image is an authentic, unaltered photograph. The **AI-vs-Deepfake-vs-Real-Siglip2** model is designed to classify images into three categories: **AI-generated, deepfake, or real**. It helps in identifying whether an image is fully synthetic, altered through deepfake techniques, or an unaltered real image.

Image classification
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The **Deep-Fake-Detector-v2-Model** is a state-of-the-art deep learning model designed to detect deepfake images. It leverages the **Vision Transformer (ViT)** architecture, specifically the `google/vit-base-patch16-224-in21k` model, fine-tuned on a dataset of real and deepfake images. The model is trained to classify images as either "Realism" or "Deepfake" with high accuracy, making it a powerful tool for detecting manipulated media. ** Update : ** The previous model checkpoint was obtained using a smaller classification dataset. Although it performed well in evaluation scores, its real-time performance was average due to limited variations in the training set. The new update includes a larger dataset to improve the detection of fake images. The model is based on the **Vision Transformer (ViT)**, which treats images as sequences of patches and applies a transformer encoder to learn spatial relationships. Key components include: - **Patch Embedding**: Divides the input image into fixed-size patches (16x16 pixels). - **Transformer Encoder**: Processes patch embeddings using multi-head self-attention mechanisms. - **Classification Head**: A fully connected layer for binary classification.

Image classification
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> `deepfake-detector-model-v1` is a vision-language encoder model fine-tuned from google/siglip-base-patch16-512 for binary deepfake image classification. It is trained to detect whether an image is real or generated using synthetic media techniques. The model uses the `SiglipForImageClassification` architecture.

Image classification
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Face-Mask-Detection

prithivMLmods

> **Face-Mask-Detection** is a binary image classification model based on `google/siglip2-base-patch16-224`, trained to detect whether a person is **wearing a face mask** or **not**. This model can be used in **public health monitoring**, **access control systems**, and **workplace compliance enforcement**.

Image classification
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GeoGuessr-55

prithivMLmods

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Image classification
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> **siglip2-x256-explicit-content** is a vision-language encoder model fine-tuned from **siglip2-base-patch16-256** for **multi-class image classification**. Built on the **SiglipForImageClassification** architecture, the model is trained to identify and categorize content types in images, especially for **explicit, suggestive, or safe media filtering**. > [!note] *SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features* https://arxiv.org/pdf/2502.14786 - **Content Moderation**: Automatically detect NSFW or suggestive content. - **Parental Controls**: Enable AI-based filtering for safe media browsing. - **Dataset Preprocessing**: Clean and categorize image datasets for research or deployment. - **Online Platforms**: Help enforce content guidelines for uploads and user-generated media.

Image classification
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> **Watermark-Detection-SigLIP2** is a vision-language encoder model fine-tuned from **google/siglip2-base-patch16-224** for **binary image classification**. It is trained to detect whether an image **contains a watermark or not**, using the **SiglipForImageClassification** architecture. > [!note] > Watermark detection works best with crisp and high-quality images. Noisy images are not recommended for validation. > [!note] *SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features* https://arxiv.org/pdf/2502.14786

Image classification
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