We are working on creating pretrained weights with NetsPresso Trainer and our own resources. We base training recipes on the official repositories or original papers to replicate the performance of models.
For models that we have not yet trained with NetsPresso Trainer, we provide their pretrained weights from other awesome repositories. We have converted several models' weights into our own model architectures. Therefore, we denote models that provide weights obtained from other repositories by appending an "*" to the model's name in the benchmark performance table. If you are interested in the original models, refer to the remarks in the table. We appreciate all the original authors and we also do our best to make other values.
If you have a better recipe, please share with us anytime. We appreciate all efforts to improve our models
| Dataset | Model | Weights | Input shape | Acc_1 | Acc_5 | Params | FLOPs | NetsPresso | Remarks |
|---|---|---|---|---|---|---|---|---|---|
| ImageNet1K | EfficientFormer-l1* | download | (224, 224) | 80.03 | 94.90 | 11.84M | 2.60G | Supported | snap-research/EfficientFormer |
| ImageNet1K | MixNet-s | download | (224, 224) | 75.08 | 92.32 | 4.13M | 0.51G | Supported | - |
| ImageNet1K | MixNet-m | download | (224, 224) | 76.37 | 93.07 | 5.01M | 0.71G | Supported | - |
| ImageNet1K | MixNet-l | download | (224, 224) | 80.78 | 95.23 | 7.33M | 1.16G | Supported | - |
| ImageNet1K | MobileNetV3-small* | download | (224, 224) | 67.67 | 87.41 | 2.54M | 0.12G | Supported | torchvision |
| ImageNet1K | MobileNetV3-large* | download | (224, 224) | 75.31 | 92.64 | 5.48M | 0.45G | Supported | torchvision |
| ImageNet1K | MobileNetV4-conv-small* | download | (224, 224) | 73.74 | 91.39 | 3.77M | 0.38G | Supported | timm |
| ImageNet1K | MobileNetV4-conv-medium* | download | (224, 224) | 79.09 | 94.67 | 9.72M | 1.68G | Supported | timm |
| ImageNet1K | MobileNetV4-conv-large* | download | (256, 256) | 81.84 | 95.74 | 32.59M | 5.72G | Supported | timm |
| ImageNet1K | MobileNetV4-hybrid-medium* | download | (224, 224) | 80.49 | 95.40 | 11.07M | 1.96G | Supported | timm |
| ImageNet1K | MobileNetV4-hybrid-large* | download | (384, 384) | 83.80 | 96.72 | 37.76M | 15.54G | Supported | timm |
| ImageNet1K | MobileViT-s* | download | (256, 256) | 78.21 | 94.13 | 5.58M | 4.07G | Supported | No input z-norm, apple/ml-cvnets |
| ImageNet1K | ResNet18 | download | (224, 224) | 69.11 | 88.87 | 11.69M | 3.64G | Supported | - |
| ImageNet1K | ResNet34 | download | (224, 224) | 72.42 | 90.87 | 21.80M | 7.34G | Supported | - |
| ImageNet1K | ResNet50 | download | (224, 224) | 79.67 | 94.82 | 25.56M | 8.22G | Supported | - |
| ImageNet1K | ViT-tiny* | download | (224, 224) | 72.90 | 91.17 | 5.70M | 2.52G | Supported | No input z-norm, apple/ml-cvnets |
| Dataset | Model | Weights | Input shape | mIoU | Pixel acc | Params | FLOPs | NetsPresso | Remarks |
|---|---|---|---|---|---|---|---|---|---|
| ADE20K | SegFormer-b0* | download | (512, ?) or (?, 512) | 37.15 | 76.78 | 3.75M | 17.01G | Supported | mmsegmentation, Resize short edge to 512 |
| Cityscapes | PIDNet-s* | download | (1024, 2048) | 78.76 | 96.12 | 7.72M | 95.03G | Supported | XuJiacong/PIDNet |
| Dataset | Model | Weights | Input shape | mAP50 | mAP75 | mAP50:95 | Params | FLOPs | NetsPresso | Remarks |
|---|---|---|---|---|---|---|---|---|---|---|
| COCO-val | RT-DETR_res18* | download | (640, 640) | 65.77 | 52.75 | 48.49 | 20.18M | 40.36G | Supported | No input z-norm, lyuwenyu/RT-DETR |
| COCO-val | RT-DETR_res50* | download | (640, 640) | 72.64 | 59.50 | 54.73 | 42.94M | 138.36G | Supported | No input z-norm, lyuwenyu/RT-DETR |
| COCO-val | yolov9-tiny | download | (640, 640) | 50.03 | 38.63 | 36.02 | 2.44M | 9.99G | Supported | No input z-norm |
| COCO-val | yolov9-s* | download | (640, 640) | 62.63 | 51.13 | 47.13 | 7.23M | 26.87G | Supported | No input z-norm, YOLO |
| COCO-val | yolov9-m* | download | (640, 640) | 67.43 | 56.13 | 51.72 | 20.12M | 77.08G | Supported | No input z-norm, YOLO |
| COCO-val | yolov9-c* | download | (640, 640) | 69.16 | 57.90 | 53.28 | 25.50M | 103.17G | Supported | No input z-norm, YOLO |
| COCO-val | YOLOX-nano* | download | (416, 416) | 41.30 | 27.90 | 26.33 | 0.91M | 1.08G | Supported | Megvii-BaseDetection/YOLOX, conf_thresh=0.01, nms_thresh=0.65 |
| COCO-val | YOLOX-tiny* | download | (416, 416) | 50.69 | 36.18 | 34.00 | 5.06M | 6.45G | Supported | Megvii-BaseDetection/YOLOX, conf_thresh=0.01, nms_thresh=0.65 |
| COCO-val | YOLOX-s | download | (640, 640) | 58.56 | 44.10 | 40.63 | 8.97M | 26.81G | Supported | conf_thresh=0.01, nms_thresh=0.65 |
| COCO-val | YOLOX-m* | download | (640, 640) | 65.00 | 51.34 | 47.04 | 25.33M | 73.76G | Supported | Megvii-BaseDetection/YOLOX, conf_thresh=0.01, nms_thresh=0.65 |
| COCO-val | YOLOX-l* | download | (640, 640) | 68.07 | 55.18 | 50.68 | 54.21M | 155.65G | Supported | Megvii-BaseDetection/YOLOX, conf_thresh=0.01, nms_thresh=0.65 |
| COCO-val | YOLOX-x* | download | (640, 640) | 69.13 | 56.46 | 51.79 | 99.07M | 281.94G | Supported | Megvii-BaseDetection/YOLOX, conf_thresh=0.01, nms_thresh=0.65 |
| COCO-val | YOLO-Fastest-v2 | download | (640, 640) | 25.03 | 11.60 | 12.78 | 0.25M | 0.74G | Supported | conf_thresh=0.01, nms_thresh=0.65 |