Instructions to use thangkt/PCB-Prune-YOLO-P40-A8-Direct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use thangkt/PCB-Prune-YOLO-P40-A8-Direct with ultralytics:
from ultralytics import YOLOvv8 model = YOLOvv8.from_pretrained("thangkt/PCB-Prune-YOLO-P40-A8-Direct") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
| parameters,counted_parameters,macs,gmacs,flops_estimate,gflops_estimate,measurement_scope,mean_latency_ms,median_latency_ms,p95_latency_ms,fps,model,model_size_mb,batch_size,imgsz,device,gpu_name,gpu_total_memory_mb,peak_gpu_memory_mb,peak_gpu_memory_measurement,python_version,torch_version,cuda_version,ultralytics_version | |
| 903466,903466,1121237600,1.1212376,2242475200,2.2424752,pure_model_forward_excludes_preprocess_and_nms,7.624854440009585,7.448529999237508,8.457600999463466,131.1500446162934,outputs/finetune_direct/p40_a8_adamw_exact_ext/weights/best.pt,1.9655094146728516,1,640,cuda:0,Tesla T4,14911.6875,21.767578125,torch_cuda_allocator,3.12.12,2.10.0+cu128,12.8,8.4.115 | |