分類
ImageNet
| Network Name | Accuracy (top1) | Quantized | Input Resolution (HxWxC) | Params (M) | MAC (G) | Pretrained | Source | Compiled |
|---|---|---|---|---|---|---|---|---|
| efficientnet_l | 80.46 | 79.36 | 300x300x3 | 10.55 | 9.70 | link | link | link |
| efficientnet_lite0 💥 | 74.99 | 73.91 | 224x224x3 | 4.63 | 0.39 | link | link | link |
| efficientnet_lite1 💥 | 76.68 | 76.21 | 240x240x3 | 5.39 | 0.61 | link | link | link |
| efficientnet_lite2 💥 | 77.45 | 76.74 | 260x260x3 | 6.06 | 0.87 | link | link | link |
| efficientnet_lite3 💥 | 79.29 | 78.33 | 280x280x3 | 8.16 | 1.40 | link | link | link |
| efficientnet_lite4 💥 | 80.79 | 80.47 | 300x300x3 | 12.95 | 2.58 | link | link | link |
| efficientnet_m 🚀 | 78.91 | 78.63 | 240x240x3 | 6.87 | 3.68 | link | link | link |
| efficientnet_s | 77.64 | 77.32 | 224x224x3 | 5.41 | 2.36 | link | link | link |
| hardnet39ds | 73.43 | 72.23 | 224x224x3 | 3.48 | 0.43 | link | link | link |
| hardnet68 | 75.47 | 75.04 | 224x224x3 | 17.56 | 4.25 | link | link | link |
| inception_v1 | 69.74 | 69.3 | 224x224x3 | 6.62 | 1.50 | link | link | link |
| mobilenet_v1 | 70.97 | 70.25 | 224x224x3 | 4.22 | 0.57 | link | link | link |
| mobilenet_v2_1.0 🚀 | 71.78 | 71.08 | 224x224x3 | 3.49 | 0.31 | link | link | link |
| mobilenet_v2_1.4 | 74.18 | 73.07 | 224x224x3 | 6.09 | 0.59 | link | link | link |
| mobilenet_v3 | 72.21 | 71.73 | 224x224x3 | 4.07 | 1.00 | link | link | link |
| mobilenet_v3_large_minimalistic | 72.11 | 71.07 | 224x224x3 | 3.91 | 0.21 | link | link | link |
| regnetx_1.6gf 💥 | 77.05 | 76.75 | 224x224x3 | 9.17 | 1.61 | link | link | link |
| regnetx_800mf 💥 | 75.16 | 74.84 | 224x224x3 | 7.24 | 0.80 | link | link | link |
| regnety_200mf | 70.38 | 69.91 | 224x224x3 | 3.15 | 0.20 | link | link | link |
| resmlp12_relu 💥 | 75.26 | 74.06 | 224x224x3 | 15.77 | 3.02 | link | link | link |
| resnet_v1_18 | 71.26 | 70.64 | 224x224x3 | 11.68 | 1.82 | link | link | link |
| resnet_v1_34 | 72.7 | 72.14 | 224x224x3 | 21.79 | 3.67 | link | link | link |
| resnet_v1_50 🚀 ⭐ | 75.12 | 74.47 | 224x224x3 | 25.53 | 3.49 | link | link | link |
| resnet_v2_18 | 69.57 | 69.1 | 224x224x3 | 11.68 | 1.82 | link | link | link |
| resnet_v2_34 | 73.07 | 72.72 | 224x224x3 | 21.79 | 3.67 | link | link | link |
| resnext26_32x4d | 76.18 | 75.78 | 224x224x3 | 15.37 | 2.48 | link | link | link |
| resnext50_32x4d | 79.31 | 78.39 | 224x224x3 | 24.99 | 4.24 | link | link | link |
| shufflenet_g8_w1 | 66.3 | 65.44 | 224x224x3 | 2.46 | 0.18 | link | link | link |
| squeezenet_v1.1 | 59.85 | 59.4 | 224x224x3 | 1.24 | 0.39 | link | link | link |
| vit_tiny | 68.02 | 65.42 | 224x224x3 | 5.41 | 2.36 | link | link | link |
- Network available in Hailo Benchmark are marked with 🚀
- Networks available in TAPPAS are marked with ⭐
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