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Image classification
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The mnistvit vision transformer (ViT), trained on MNIST with 99.65% test set accuracy, packaged for the Hugging Face transformers library. The weights are a lossless conversion of the original checkpoint. The model itself is the standard transformers `ViTModel` with a small classification head, so no dependencies beyond transformers are needed. An ONNX export in `onnx/model.onnx` runs without Python or PyTorch, for example with ONNX Runtime in C++, C#, Java, mobile apps or the browser. The model and its image processor are defined in this repository, so loading requires `trust_remote_code=True`:

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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Vision Transformer (ViT-Small) trained on 2.7M samples across 4,803 generators for detecting AI-generated images. Presented in Community Forensics: Using Thousands of Generators to Train Fake Image Detectors (CVPR 2025). **Uploaded for community validation as part of OpenSight** — An upcoming open-source framework for adaptive deepfake detection. **Project OpenSight HF Spaces coming soon with an eval playground and eventually a leaderboard. Preview:**

Image classification
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Binary image classifier: `rain` vs `no_rain`, for a camera looking out a dock door. Fine-tuned from facebook/convnext-tiny-224 (Apache-2.0). Part of Kairos, a weather plugin for Boston Dynamics Spot fleets (Knight Hacks IX hackathon build). It feeds one input of a GO / HOLD rule. Python: `pipeline("image-classification", model="builtbyd3v/kairos-door-rain")`. Hold when p(rain) >= **0.4447** for q8, or >= **0.4728** for fp32 (each picked on the calibration split, one image per call). Both are in `eval_results.json` under `thresholds`. 95% CIs are Wilson score intervals. Threshold rule: the highest threshold that keeps calibration rain recall >= 95%, rounded down. Scored with onnxruntime CPU on the shipped ONNX files, one image per call (as in the browser).

Image classification
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Binary image classifier: `wet` (visible standing water or a wet surface) vs `dry`. Fine-tuned from facebook/convnext-tiny-224 (Apache-2.0). Built for SPOT Dispatch, a readiness board for Boston Dynamics Spot inspection routes. Spot's user guide says not to walk on wet or slippery surfaces, so the board can flag route photos that look wet for an operator to review. `onnx/model_quantized.onnx` (33.8 MB vs 111.4 MB fp32) loads with `{ dtype: "q8" }`. It is onnxruntime `quantize_dynamic`, uint8 weights, MatMul/Gemm layers only (convs stay fp32). Scored with onnxruntime on the same test split: q8 misses one more same-source wet image and changes 1 of 752 test predictions vs fp32 (kit images: same calls). Use fp32 when recall matters more than load time. Quantizing the convs too (28 MB) lost 2 same-source wet images, so it is not shipped.

Image classification
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A Swin-B image feature model. Supervisely pre-trained on animal re-identification datasets. - **Model Type:** Animal re-identification / feature backbone - **Model Stats:** - Params (M): 109.1 - Image size: 224 x 224 - Architecture: swin_base_patch4_window7_224 - **Paper:** WildlifeDatasets_An_Open-Source_Toolkit_for_Animal_Re-Identification - **Related Papers:** - Swin Transformer: Hierarchical Vision Transformer using Shifted Windows - DINOv2: Learning Robust Visual Features without Supervision - **Pretrain Dataset:** All available re-identification datasets --> https://github.com/WildlifeDatasets/wildlife-datasets

Image classification
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A CLIP based image feature model. Supervisely pre-trained on animal re-identification datasets. Original model: timm/vit_large_patch14_clip_336.openai_ft_in12k_in1k - **Model Type:** Animal re-identification / feature backbone - **Model Stats:** - Params (M): 304.5 - GMACs: 174.7 - Activations (M): 128.2 - Image size: 336 x 336

Image classification
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