Image Classification Models

Model · Image classification

pixai-tagger-v1.0-onnx-fp32-fp16-int8

Mexes GM

Unofficial ONNX export of pixai-labs/pixai-tagger-v1.0 in FP32, FP16 and INT8. Runs with ONNX Runtime only. - Input input: float32 [batch, 3, 1008, 1008], RGB, NCHW. - Output prediction: float32 [batch, 30877], probabilities (sigmoid is part of the graph). - Opset 17. training cutoff, and are also scored against their Danbooru tags. - FP16 is meant for GPUs. On CPU it is slower than FP32, because the CPU has no native FP16 math. - FP32 fits on a 4 GB GPU only barely (3.2 GB VRAM), and its speed varies between runs. - INT8 speed depends on CPU support for VNNI instructions. On older CPUs it can be slower than FP32. - Peak RAM on CPU includes session initialization; FP32 briefly reaches 9.4…

This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Binary classifier (NORMAL vs PNEUMONIA): torchvision ResNet50 (ImageNet IMAGENET1KV2 weights) fully fine-tuned on Purino/chest-xray-pneumonia-256-fullframe-gray. Research/educational use only, not a diagnostic tool. Confusion matrix (rows = true, columns = predicted): Validation (15% stratified hold-out of train, 783 images) at the selected epoch 8: accuracy 0.9885, recall 0.9880, macro-F1 0.9851, AUC 0.9983. - The dataset has no patient-level split or deduplication, so validation (and possibly test) images may come from patients seen in training; scores can be optimistic. - Trained on one pediatric dataset (Kermany et al.); it will likely not generalize to other scanners, populations or…

本仓库提供由 tensorflow/models 转换得到的 RKNN CNN 模型。 - Model ID：RKNNAI/RK182X-CNN-MobileNetV1 - 模型显示名称：RK182X-CNN-MobileNetV1 - 源模型：tensorflow/models - 模型类型：CNN ModelScope 完整下载： Hugging Face 完整下载： ModelScope 指定配置下载： Hugging Face 指定配置下载： - 使用配套 RKNN Runtime 和驱动；运行前用 rknn-smi -v 检查设备端版本。 - 源模型许可证：Apache License 2.0，正文见 LICENSE，归属和转换说明见 NOTICE。 - 本仓库交付物是源模型的 RKNN 转换衍生物，仍受源模型许可和使用限制约束。 - 同时遵守 RKNN Toolkit、RKNN Runtime 相关许可；本仓库不包含运行库。

本仓库提供由 onnx/models 转换得到的 RKNN CNN 模型。 - Model ID：RKNNAI/RK182X-CNN-MobileNetV2 - 模型显示名称：RK182X-CNN-MobileNetV2 - 源模型：onnx/models - 模型类型：CNN ModelScope 完整下载： Hugging Face 完整下载： ModelScope 指定配置下载： Hugging Face 指定配置下载： - 使用配套 RKNN Runtime 和驱动；运行前用 rknn-smi -v 检查设备端版本。 - 源模型许可证：Apache License 2.0，正文见 LICENSE，归属和转换说明见 NOTICE。 - 本仓库交付物是源模型的 RKNN 转换衍生物，仍受源模型许可和使用限制约束。 - 同时遵守 RKNN Toolkit、RKNN Runtime 相关许可；本仓库不包含运行库。

本仓库提供由 onnx/models 转换得到的 RKNN CNN 模型。 - Model ID：RKNNAI/RK182X-CNN-ResNet50V2 - 模型显示名称：RK182X-CNN-ResNet50V2 - 源模型：onnx/models - 模型类型：CNN ModelScope 完整下载： Hugging Face 完整下载： ModelScope 指定配置下载： Hugging Face 指定配置下载： - 使用配套 RKNN Runtime 和驱动；运行前用 rknn-smi -v 检查设备端版本。 - 源模型许可证：Apache License 2.0，正文见 LICENSE，归属和转换说明见 NOTICE。 - 本仓库交付物是源模型的 RKNN 转换衍生物，仍受源模型许可和使用限制约束。 - 同时遵守 RKNN Toolkit、RKNN Runtime 相关许可；本仓库不包含运行库。

本仓库提供由 google/vit-base-patch16-224 转换的 RKNN 模型。 - Model ID：RKNNAI/RK182X-VIT-vit-base-patch16-224 - 模型显示名称：RK182X-VIT-vit-base-patch16-224 - 源模型：google/vit-base-patch16-224 - 模型类型：VIT ModelScope 完整下载： Hugging Face 完整下载： ModelScope 指定配置下载： Hugging Face 指定配置下载： - 使用配套 RKNN Runtime 和驱动；运行前用 rknn-smi -v 检查设备端版本。 - 源模型许可证：Apache License 2.0，正文见 LICENSE，归属与转换修改说明见 NOTICE。 - 同时遵守 RKNN Toolkit、RKNN Runtime 相关许可；本包不包含运行库。

Model forensik Hunter berbasis ConvNeXtV2-Tiny yang dilatih murni dari awal (from scratch / zero pretraining) dengan mekanisme penalti asimetris untuk meminimalkan False Negative pada citra sintetis AI. - Weight Real (0): 1.0 - Weight AI (1): 3.0 (Hukuman 3x lipat jika AI lolos)

This model classifies facial skin images into 6 common dermatological conditions using a fine-tuned EfficientNetV2B0 architecture. - Evaluated on a balanced, augmented custom dataset of real-world dermatological images. - Confusion Matrix indicates strong separation for all classes, with minor overlap in visually similar conditions. - Global Average Pooling + Dense(512, ReLU) + Dropout(0.4) + Dense(6, Softmax) - Fine-tuned with learning rate scheduling and layer unfreezing Softmax probabilities for 6 classes. argmax(pred) gives class index from 0–5. Tanishq Shinde B.E. Computer Engineering, PICT

Model · Image classification

skin-lesion-classifier-resnet18

Ashraf

Trained on HAM10000. Educational project — not diagnostic-grade, not a medical device.

EfficientNetV2-L (torchvision efficientnetv2l, 1000 ImageNet classes) fine-tuned with adversarial training for the Perturb subnet (netuid 26).

A 153-species arachnid classifier (spiders, scorpions, ticks, harvestmen) small enough for microcontrollers and the Coral Edge TPU. MobileNetV2 at full width and 160 px, full-integer int8, trained on the iNaturalist 2021 competition data. It fills the gap people hit first with Google's AIY insect model, which has no spiders. Part of species-classifiers. Measured on the int8 file over the 1,530 iNaturalist 2021 validation images for these species (10 per species), with an 87.5% centre crop: The PyTorch checkpoint scores 64.4% top-1. For comparison, Google's stock AIY insect model scores 59.1% top-1 on the iNat2021 species it shares, choosing among 1,022 classes. - Input [1, 160, 160, 3]…

A 246-species mammal classifier small enough for microcontrollers and the Coral Edge TPU. MobileNetV2 at full width and 160 px, full-integer int8, trained on the iNaturalist 2021 competition data. Part of species-classifiers, which also rescues Google's AIY Vision Kit insect, plant and bird models into the same shape. Measured on the int8 file over the 2,460 iNaturalist 2021 validation images for these species (10 per species), with an 87.5% centre crop: The PyTorch checkpoint scores 56.1% top-1; int8 costs about a point. Grayscale is reported because the model was trained with 20% random grayscale so it holds up on monochrome cameras such as the Coral Dev Board Micro's. For comparison…

This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 5e-05 - trainbatchsize: 32 - evalbatchsize: 32 - gradientaccumulationsteps: 4 - totaltrainbatchsize: 128 - lrschedulertype: linear - numepochs: 3 - Transformers 5.18.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2

This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 5e-05 - trainbatchsize: 32 - evalbatchsize: 32 - lrschedulertype: linear - numepochs: 3 - Transformers 5.18.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2

Model · Image classification

tinyvit-5m-int8-imagenet

Core Epoch

TinyViT-5M (timm/tinyvit5m224.distin22kftin1k, Apache-2.0) quantized to INT8 with Kenosis — 128-image calibration, no retraining. 80.53% top-1 from a 9.2 MB single file, on ONNX Runtime or OpenVINO, CPU or GPU, no accelerator required. ImageNet-1K validation, 49,872 images (disjoint from the 128 calibration images). Measured on a CPU with AVX-VNNI; on CPUs without VNNI this model's INT8 top-1 sits ~0.9 below FP32 rather than 0.34. Input 1x3x224x224, RGB, /255, ImageNet mean/std. Output logits [1,1000], sorted-synset order. runclassify.py / evalimagenet.py reproduce the demo and table. tinyvit5m224int8kenosis.onnx (9,228,567 B) — SHA-256…

Official model checkpoints for the solution in the Traffic Sign Recognition under Adverse Weather Competition. See classes.txt for the 25 traffic sign classes. For inference scripts, training code, and in-depth engineering retrospective, visit the GitHub Repository.