convnext_femto.d1_in1kModel39/100 via “imagenet-1k pre-trained image classification with convnext femto architecture”
image-classification model by undefined. 4,98,269 downloads.
Unique: ConvNeXt Femto is the smallest variant in the ConvNeXt family (~4.7M parameters) designed specifically for efficient inference, using modern CNN design principles (depthwise convolutions, layer norm, GELU) that were previously exclusive to Vision Transformers. The safetensors distribution format enables safe, reproducible model loading without pickle deserialization vulnerabilities. Trained via the timm library's standardized pipeline, ensuring compatibility with 500+ other pre-trained models in the same ecosystem.
vs others: Smaller and faster than MobileNetV3 (5.4M params) while maintaining comparable ImageNet accuracy (~80%), and more efficient than ViT-Tiny (5.7M params) due to CNN inductive bias; unlike EfficientNet, uses modern normalization techniques that improve transfer learning performance on downstream tasks.