Instructions to use thangkt/PCB-Prune-YOLO-P30-Direct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use thangkt/PCB-Prune-YOLO-P30-Direct with ultralytics:
from ultralytics import YOLOvv8 model = YOLOvv8.from_pretrained("thangkt/PCB-Prune-YOLO-P30-Direct") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
| { | |
| "parameters": 1452562, | |
| "counted_parameters": 1452562, | |
| "macs": 1961891200, | |
| "gmacs": 1.9618912, | |
| "flops_estimate": 3923782400, | |
| "gflops_estimate": 3.9237824, | |
| "mean_latency_ms": 9.863123440172785, | |
| "median_latency_ms": 9.747029500431381, | |
| "p95_latency_ms": 11.026255997421686, | |
| "fps": 101.38776079055964, | |
| "model": "outputs/finetune_direct/p30_adamw_exact/weights/best.pt", | |
| "model_size_mb": 3.0141544342041016, | |
| "batch_size": 1, | |
| "imgsz": 640, | |
| "device": "cuda:1", | |
| "gpu_name": "Tesla T4", | |
| "gpu_total_memory_mb": 14911.6875, | |
| "peak_gpu_memory_mb": 29.1787109375, | |
| "python_version": "3.12.12", | |
| "torch_version": "2.10.0+cu128", | |
| "cuda_version": "12.8", | |
| "ultralytics_version": "8.4.115" | |
| } |