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Explore AI models 1529
convnext-tiny-224
Xenova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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View details ↗https://huggingface.co/dima806/facial_emotions_image_detection with ONNX weights to be compatible with Transformers.js. Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using 🤗 Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
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View details ↗mobilevit-small
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View details ↗mobilevit-xx-small
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View details ↗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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View details ↗resnet-18
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View details ↗resnet-50
Xenova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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View details ↗vit-base-patch16-224
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View details ↗pvt-tiny-224
XrenyaPyramid Vision Transformer (PVT) model pre-trained on ImageNet-1K (1 million images, 1000 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions by Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao and first released in this repository. Disclaimer: The team releasing PVT did not write a model card for this model so this model card has been written by [Rinat S. [@Xrenya]](https://huggingface.co/Xrenya). The Pyramid Vision Transformer (PVT) is a transformer encoder model (BERT-like) pretrained on ImageNet-1k (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, also at resolution 224x224.
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View details ↗mahjong_soul_vision-onnx
xzhe121An ONNX export of pjura/mahjong_soul_vision, a ViT-base image classifier its author fine-tuned to tell the 34 Japanese mahjong tile kinds apart on Mahjong Soul screen crops. Hosted here because the original repository only has PyTorch weights; it is used by an offline mahjong study aid that reads the game window and never touches the game. Apache License 2.0, as the original model; see `LICENSE`. The model was trained by its original author on screenshots of the game Mahjong Soul (pjura/mahjong_souls_tiles). The game art belongs to its owners. This repository contains no images. If you hold rights to the game and want it removed, open a discussion and it will be taken down.
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View details ↗GenD_CLIP_L_14
yermandy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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View details ↗GenD_DINOv3_L
yermandy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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View details ↗GenD_PE_L
yermandy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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View details ↗print-defect-cnn
Zainulabedin1A small convolutional neural network that classifies a photo of an FDM 3D print as a **good print** or a **stringing defect** (thin strands of filament left between printed sections — wasted material and post-processing time). 106k parameters, trained from scratch on CPU; input is a 64×64 grayscale image. Per-class F1 — good print: 80.56% · stringing: 97.60%. At the default threshold the model catches **every** stringing defect in the test set (100% recall on the defect class) at the cost of some false alarms on good prints — the right trade-off for defect screening, where a missed defect wastes a whole print. Preprocessing contract (must match training): convert to grayscale, resize to 64×64, `ToTensor()`, `Normalize(mean=0.5, std=0.5)`. The helper `predict_image` does this for you.
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View details ↗vit-casting-defect
Zainulabedin1A **tiny Vision Transformer trained from scratch** (no pretraining, no ImageNet) that classifies steel surface-defect images into six defect classes. The point of the project is to show a ViT *can* be built for manufacturing and trained on CPU: 64x64 image -> 64 patches of 8x8 -> 2-layer transformer encoder -> class, ~113k parameters, trained on CPU in minutes. Per-class F1 (TinyViT): crazing 85.71% · patches 91.23% · pitted_surface 75.86% · inclusion 68.85% · scratches 68.97% · rolled-in_scale 66.67%. **Honest reading:** the small CNN wins by ~9 points — convolutional inductive bias beats a from-scratch transformer on a 1,440-image dataset. The ViT is still the point of the project: 113k parameters, no pretraining, CPU-trained in ~14 minutes, and 76% is far above the 16.7% chance level. On larger industrial datasets (or with a conv stem) the gap closes; see "What I'd try next" in the notebook.
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View details ↗pvt-tiny-224
Zetatech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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