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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. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Image classification
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Smaug-AI-Image-Detector sets a new state of the art for open-source AI-generated image detection. It is a fine-tune of Google's EmbeddingGemma-2 by Abacus.AI and the strongest open-source detector on real-world images. **A dedicated vision model, 41% smaller than its base.** We streamlined EmbeddingGemma-2 into a vision-only architecture by removing its 305M-parameter audio tower: 439M parameters instead of 744M, with no loss in accuracy. Architecture EmbeddingGemma-2 image encoder + linear classifier Total Parameters 439M (base model: 744M) Audio Tower Removed: vision-only architecture, −305M parameters Input Any image; scored as three 528 × 528 crops at native resolution Output Probability that the image is AI-generated Hardware NVIDIA GPU (~1 GB), Apple Silicon (Metal), or CPU Precision bfloat16 weights Formats PyTorch / sentence-transformers (this repo); GGUF for llama.cpp: Smaug-AI-Image-Detector-GGUF Base Model google/embeddinggemma-2 Adaptation Full fine-tune

Image classification
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A state-of-the-art Vision Transformer model for simultaneous age estimation and gender classification, achieving **94.3% gender accuracy** and **4.5 years age MAE** on the UTKFace dataset. ✅ **Issue Resolved:** Model now includes helper functions that return proper age and gender values (not `LABEL_0`/`LABEL_1`) **Important:** These helper functions work correctly. The standard `pipeline()` approach returns `LABEL_0`/`LABEL_1` and should not be used.

Image classification
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beans-deit-tiny

Adamix3003

This model is a fine-tuned version of facebook/deit-tiny-patch16-224 on the AI-Lab-Makerere/beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0988 - Accuracy: 0.9699 - F1 Macro: 0.9699 The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP

Image classification
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This model is a fine-tuned version of facebook/deit-tiny-patch16-224 on the AI-Lab-Makerere/beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0619 - Accuracy: 0.9850 - F1 Macro: 0.9850 The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP

Image classification
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Recognises one handwritten Gurmukhi digit (੦ ੧ ੨ ੩ ੪ ੫ ੬ ੭ ੮ ੯) in an image. It is the model behind the digit recognition on AI Panjab. **Try it without code:** the Space: draw a digit and get the top three guesses. The Space also has a **Use via API** page (Python, JavaScript, curl). 1. a transparent background (RGBA) is put on white; any other image is converted to RGB; 2. grayscale with the ITU-R 601 weights: `0.2989 R + 0.5870 G + 0.1140 B`, kept in floating point; 3. resized to 28 x 28 with bilinear (antialiased) resizing.

Image classification
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Model CNN hasil *fine-tuning* ResNet-50 (pra-terlatih ImageNet) untuk mengklasifikasikan gambar pemandangan ke dalam 6 kelas: buildings, forest, glacier, mountain, sea, street. Intel Image Classification (Kaggle) — sekitar 14 ribu gambar latih (dibagi 85% train / 15% validasi) dan 3 ribu gambar uji, ukuran 150×150.

Image classification
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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. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Image classification
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