- Object Detection
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 | mAP(0.5) (%) | mAP(0.5:0.95)(%) | FLOPs (M) | Params (M) | Latency (ms) | Model Size (MB) |
|---|---|---|---|---|---|---|---|---|---|
| YOLOX | Original | COCO | 68.0 | 49.7 | 156006.20 | 54.21 | 12239.46 | 207.37 | |
| YOLOX | Google Colab | Compressed-1 | COCO | 67.16 (-0.84) | 48.64 (-1.06) | 101804.06 (1.53x) | 19.96 (2.7x) | 8502.72 (1.44x) | 76.61 (2.7x) |
| YOLOX | Google Colab | Compressed-2 | COCO | 61.43 (-6.57) | 43.23 (-5.47) | 38607.03 (4.04x) | 4.93 (11.0x) | 4235.37 (2.89x) | 19.17 (10.80x) |
- 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 | mAP(0.5) (%) | mAP(0.5:0.95)(%) | FLOPs (M) | Params (M) | Latency (ms) | Model Size (MB) |
|---|---|---|---|---|---|---|---|---|---|
| YOLOv4 | Original | PASCAL VOC | 82.22 | - | 61871.82 | 65.32 | 64318.70 | 262.90 | |
| YOLOv4 | Google Colab | Compressed-1 | PASCAL VOC | 87.23 (+5.01) | - | 11459.69 (5.4x) | 10.59 (6.17x) | 28651.70 (2.16x) | 44.12 (5.96x) |
| YOLOv4 | Google Colab | Compressed-2 | PASCAL VOC | 87.91 (+5.69) | - | 14442.96 (4.28x) | 10.71 (6.1x) | 28976.40 (2.14x) | 44.36 (5.93x) |
- YOLOv4 model with EfficientNet B1 based backbone.
- The model’s latency is measured on Raspberry Pi 4B (1.5GHz ARM Cortex).
- Options: FP32, TFLite
Updated 5 months ago