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This model is a fine-tuned version of facebook/dinov2-base on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0125 - Accuracy: 0.9965 The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 15

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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mnist-mlp

dacorvo

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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custom-resnet50d

Dadigua12581

A Transformers-compatible wrapper around the pretrained `timm/resnet50d.ra2_in1k` ImageNet-1k classifier, following the custom model tutorial. The pretrained weights are copied from timm without additional training. The model has 25,576,264 parameters and predicts 1,000 ImageNet classes. The repository includes the custom Python code, safetensors weights, configuration, ImageNet class names, and image processor. The processor converts images to RGB, resizes the shorter edge to 256 pixels using bicubic interpolation, center-crops to 224 x 224, rescales to [0, 1], and normalizes with ImageNet mean and standard deviation.

Image classification
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The model is based on `facebook/deit-tiny-patch16-224` and was fine-tuned on a subset of the MineralImage5K-98 dataset. For this experiment, 10 mineral classes were selected. The training set was balanced to 282 images per class, resulting in 2820 training images. The model was trained on a relatively small subset of the original dataset and only covers 10 mineral classes. Performance may therefore be lower on minerals, image conditions, or classes not represented in the training data.

Image classification
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moderator

desert-ant-labs

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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shapes

desert-ant-labs

Turn a rough circle drawing into a clean shape, with fitted vector geometry ready to snap to. Built for PencilKit-style "smart shapes": draw, pause, and the rough stroke becomes a crisp shape. The model is tiny (**200KB** Core ML, **1.3MB** LiteRT) and runs in a few milliseconds on device. `line`, `rectangle`, `triangle`, `ellipse`, `star`, plus `none` (the reject class: scribbles, partial shapes, and other non-shape strokes). Squares and circles are covered by `rectangle` and `ellipse` (snapped when near-regular). Desert Ant Labs Source-Available License. Free for most apps, and a commercial license is required at scale. Full terms are at the link. Licensing: .

Image classification
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vit-beans

diegomartinezp

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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Beware that this model is trained on a dataset collected about 2 years ago. Since then, there is a remarkable progress in generating deepfake images with common AI tools, resulting in a significant concept drift. To mitigate that, I urge you to retrain the model using the latest available labeled data. As a quick-fix approach, simple reducing the threshold (say from default 0.5 to 0.1 or even 0.01) of labelling image as a fake may suffice. However, you will do that at your own risk, and retraining the model is the better way of handling the concept drift.

Image classification
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