Swin Transformer v2 model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in the paper Swin Transformer V2: Scaling Up Capacity and Resolution by Liu et al. and first released in this repository. Disclaimer: The team releasing Swin Transformer v2 did not write a model card for this model so this model card has been written by the Hugging Face team. The Swin Transformer is a type of Vision Transformer. It builds hierarchical feature maps by merging image patches (shown in gray) in deeper layers and has linear computation complexity to input image size due to computation of self-attention only within each local window (shown in red). It can thus serve as a general-purpose…
Open weights
apache-2.0
transformers
NOTE: Unless you are trying to detect imagery generated using older models such as VQGAN+CLIP, please use the updated version of this detector instead. This model is a proof-of-concept demonstration of using a ViT model to predict whether an artistic image was generated using AI. It was created in October 2022, and as such, the training data did not include any samples generated by Midjourney 5, SDXL, or DALLE-3. It still may be able to correctly identify samples from these more recent models due to being trained on outputs of their predecessors. Furthermore the intended scope of this tool is artistic images; that is to say, it is not a deepfake photo detector, and general computer imagery…
Open weights
cc-by-4.0
transformers
Measured on device (edge-compat): Raspberry Pi 5 · LiteRT 2.2.0.dev20260804 · CPU/XNNPACK, 4 threads · 34.1 ms p50 (2026-08-31); browser · Chromium 151 (2026-08-11). Record: https://github.com/john-rocky/edge-compat/blob/main/cards/plantnet-300k-resnet18/CARD.md On-device fine-grained plant species identification — 1081 species — running fully on the LiteRT CompiledModel GPU delegate (no CPU fallback). A PlantNet-300K (NeurIPS 2021) ResNet18. ~16 ms/frame on a Pixel 8a. (mean [0.485,0.456,0.406], std [0.229,0.224,0.225]; center-crop then resize 224). Labels: class index i maps to the i-th species when the PlantNet-300K species-id strings are sorted (torchvision ImageFolder order); names…
Open weights
apache-2.0
litert
Model · Image classification
Ashraf
Trained on HAM10000. Educational project — not diagnostic-grade, not a medical device.
Open weights
mit
Y
Model · Image classification
Yy
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.
Open weights
mit
timm
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…
Open weights
apache-2.0
onnx
