- Object Detection
Using automatic compression allows for easy model compression without a deep understanding of compression. Advanced compression offers higher flexibility in settings and may yield better compression results.
- 'Compressed' Type: Automatic Compression
- 'Compressed(Adv.)' Type: Advanced Compression
All of the original and compressed models can be downloaded easily on the Model Compressor Model Zoo.
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 9 months ago