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--- library_name: segmentation-models-pytorch license: other pipeline_tag: image-classification tags: - segmentation-models-pytorch - image-classification - pytorch - se_resnext languages: - python --- This repository contains the `imagenet` pre-trained weights for the `se_resnext50_32x4d` model used as encoder in the segmentation-models-pytorch library.

Image classification
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--- library_name: segmentation-models-pytorch license: other pipeline_tag: image-classification tags: - segmentation-models-pytorch - image-classification - pytorch - efficientnet languages: - python --- This repository contains the `noisy-student` pre-trained weights for the `timm-efficientnet-b4` model used as encoder in the segmentation-models-pytorch library.

Image classification
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This repository hosts the **efficientnet-v2-s** models exported for the React Native ExecuTorch library as ExecuTorch `.pte` programs, ready to run on device. A backend directory carrying a `NOTES.md` and no `.pte` was exported and not published. Its note records what went wrong. These files are published for the **ExecuTorch v1.4.1** runtime. ExecuTorch gives no forward compatibility guarantee, so an older runtime may fail to load them.

Image classification
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Inception V3 model for automated classification of plankton and other particles imaged by the Imaging FlowCytobot (IFCB) on the Northeast U.S. Shelf (NES). Classifies 155 categories including phytoplankton, microzooplankton, detritus, and imaging artifacts. This model is intended for automated taxonomic classification of IFCB imagery collected on the Northeast U.S. Shelf. It is suitable for operational use in plankton monitoring pipelines. Performance may degrade on IFCB data from other geographic regions or instruments with significantly different optical configurations. The main "cuda optimized" and "cpu" model versions return equivalent results. Both models will run fine on cpu or gpu.

Image classification
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lab1-cifar10

Springman

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: 0.8905; test accuracy: 0.8815. 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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TensorFlow and TFLite conversion of rizvandwiki/gender-classification-2 (ViT). Please check the original model card for license and training details. Note: converted with SELECT_TF_OPS, so on Android add `org.tensorflow:tensorflow-lite-select-tf-ops`.

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lab1-cifar10-mobilenet

SzymonNabrzuchowski

A torchvision MobileNetV3 Small initialized with ImageNet pretrained weights and fine-tuned on CIFAR-10 using a GPU in Google Colab. The final classification layer predicts 10 classes: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck. The checkpoint was selected using validation accuracy. The test set was evaluated after checkpoint selection.

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A locally trained vehicle classifier covering **239 model families across 42 manufacturers**. Its 346 leaf labels combine 196 original generation categories and 150 added model-only families. Generation and year fields are null for the added families; it does not infer trims or invent production years. **Status:** 30 expanded-training epochs completed; calibration, held-out test, safetensors export and exported CPU inference verified on October 18, 2026. Original implementation code, acquisition/preparation, training, evaluation and inference modules are included. No external inference service is used. Evaluated on **11,683 images**, after checkpoint selection on a separate validation split and calibration on 4,677 separate images.

Image classification
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stanford-car-vit-patch16

therealcyberlord

This achieves around 86% on the testing set, you can use it as a baseline for further tuning. The Stanford car dataset contains 16,185 images of 196 classes of cars. Classes are typically at the level of Make, Model, Year, e.g. 2012 Tesla Model S or 2012 BMW M3 coupe. The data is split into 8144 training images, 6,041 testing images, and 2000 validation images in this case. 3D Object Representations for Fine-Grained Categorization Jonathan Krause, Michael Stark, Jia Deng, Li Fei-Fei 4th IEEE Workshop on 3D Representation and Recognition, at ICCV 2013 (3dRR-13). Sydney, Australia. Dec. 8, 2013.

Image classification
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