From the model card
Fields below are copied from the tags and counters on the HuggingFace repository timm/edgenext_small.usi_in1k at our last fetch. They are set by the uploader, not verified by us; rows with no tag are omitted. How this page is made.
- Publisher (HF namespace)
- timm
- Pipeline tag
- image-classification
- Library
- timm, Transformers
- Framework tags
- PyTorch
- Weight formats
- safetensors
- License tag
mit— read the license file in the repo before relying on it- Papers cited
- arXiv:2206.10589, arXiv:2204.03475
- Datasets declared
- imagenet-1k
- Downloads (HF counter at last fetch)
- 361,500
- Likes (HF counter at last fetch)
- 6
- Model card
- https://huggingface.co/timm/edgenext_small.usi_in1k
Use cases
- Mobile image classification with hybrid CNN-transformer efficiency
- Edge device deployment requiring fast, low-power inference
- Backbone for lightweight object detection in embedded systems
- Ablation study on CNN-transformer architecture tradeoffs
Pros
- MIT license
- Hybrid architecture balances accuracy and mobile inference efficiency
- ImageNet-1K pretrained — broad visual transfer
- timm registry integration for easy fine-tuning
Cons
- timm dependency required — not native Transformers pipeline
- Small scale trades accuracy for speed — not competitive on challenging benchmarks
- USI training methodology is less documented than standard distillation
- Limited community fine-tuning examples compared to ViT or ConvNeXt families
Tags
timmpytorchsafetensorsimage-classificationtransformersdataset:imagenet-1karxiv:2206.10589arxiv:2204.03475license:mitregion:us