- Image Classification
All of the original and compressed models can be downloaded easily on the Model Compressor Model Zoo.
You can get Compressed results with Automatic Compression and Compressed (Adv.) results with Advanced Compression.
PyTorch
| Model | Best Practice | Type | Dataset | Accuracy (%) | FLOPs (M) | Params (M) | Latency (ms) | Model Size (MB) |
|---|---|---|---|---|---|---|---|---|
| VGG16 | Original | CIFAR100 | 74.00 | 629.76 | 15.30 | 71.65 | 59.65 | |
| VGG16 | Google Colab | Compressed-1 | CIFAR100 | 72.22 (-1.78) | 431.84 (1.46x) | 5.16 (2.96x) | 24.52 (2.91x) | 20.22 (2.95x) |
| VGG16 | Google Colab | Compressed-2 | CIFAR100 | 68.01 (-5.99) | 213.06 (2.96x) | 1.25 (12.26x) | 11.34 (6.32x) | 4.93 (12.10x) |
| MobileNetV2 | Original | CIFAR100 | 74.29 | 189.30 | 2.35 | 46.26 | 8.98 | |
| MobileNetV2 | Google Colab | Compressed | CIFAR100 | 73.68 (-0.61) | 119.09 (1.59x) | 0.82 (2.88x) | 24.50 (1.89x) | 3.38 (2.66x) |
| RepVGG | Original | CIFAR100 | 76.44 | 1715.70 | 12.94 | 248.10 | 50.33 | |
| RepVGG | Google Colab | Compressed-1 | CIFAR100 | 74.92 (-1.52) | 1644.88 (1.04x) | 10.64 (1.22x) | 113.35 (2.19x) | 41.81 (1.20x) |
| RepVGG | Google Colab | Compressed-2 | CIFAR100 | 69.84 (-4.60) | 721.77 (2.38x) | 2.95 (4.39x) | 51.69 (4.80x) | 11.71 (4.30x) |
| ViT | Original | CIFAR100 | 94.42 | 33725.76 | 85.80 | 1396.53 | 327.43 | |
| ViT | Google Colab | Compressed | CIFAR100 | 93.30 (-1.12) | 14804.95 (2.28x) | 37.78 (2.27x) | 737.11 (1.89x) | 144.32 (2.27x) |
- The model’s latency is measured on Raspberry Pi 4B (1.5GHz ARM Cortex).
- Options: FP32, ONNX runtime
TensorFlow-Keras
| Model | Best Practice | Type | Dataset | Accuracy (%) | FLOPs (M) | Params (M) | Latency (ms) | Model Size (MB) |
|---|---|---|---|---|---|---|---|---|
| VGG19 | Original | CIFAR-100 | 72.28 | 796.79 | 20.09 | 189.31 | 78.69 | |
| VGG19 | Google Colab | Compressed | CIFAR-100 | 71.13 (-1.15) | 132.20 (6.03x) | 1.17 (17.13x) | 12.85 (14.73x) | 4.98 (15.80x) |
| VGG19 | Compressed (Adv.) | CIFAR-100 | 71.14 (-1.14) | 100.09 (7.96x) | 0.66 (30.38x) | 4.5 (42.06x) | 5.68 (13.85x) | |
| ResNet50 | Original | CIFAR-100 | 78.03 | 2596.06 | 23.71 | 450.14 | 93.31 | |
| ResNet50 | Google Colab | Compressed | CIFAR-100 | 76.92 (-1.11) | 613.43 (4.23x) | 2.64 (8.99x) | 130.39 (3.45x) | 9.83 (9.49x) |
| ResNet50 | Compressed (Adv.) | CIFAR-100 | 76.63 (-1.4) | 224.70 (11.55x) | 2.17 (10.91x) | 48.37 (9.31x) | 18.35 (5.09x) | |
| MobileNet V1 | Original | CIFAR-100 | 66.68 | 92.90 | 3.31 | 35.61 | 13.28 | |
| MobileNet V1 | Google Colab | Compressed | CIFAR-100 | 66.32 (-0.36) | 26.09 (3.56x) | 0.53 (6.24x) | 3.66 (9.73x) | 2.38 (5.58x) |
| MobileNet V1 | Compressed (Adv.) | CIFAR-100 | 66.11 (-0.57) | 17.90 (5.19x) | 0.35 (9.35x) | 2.08 (17.12x) | 3.3 (4.02x) |
- The model’s latency is measured on Raspberry Pi 4B (1.5GHz ARM Cortex).
- Options: FP32, TensorFlow Lite
Updated 5 months ago