faster-coco-eval

import os import faster_coco_eval # Option 1: Use faster_coco_eval as a drop-in replacement for pycocotools faster_coco_eval.init_as_pycocotools() from pycocotools.coco import COCO from pycocotools.cocoeval import COCOeval # Create dummy COCO JSON files (replace with your actual paths) # Example structure based on common COCO format expectations # In a real scenario, you'd load these from actual files. anno_json_path = "annotations_val2017.json" # Path to your ground truth annotations pred_json_path = "results_predictions.json" # Path to your model's predictions # Simulate creating dummy JSON files for demonstration # In practice, these files would already exist. if not os.path.exists(anno_json_path): with open(anno_json_path, 'w') as f: f.write('{"images": [], "annotations": [], "categories": []}') if not os.path.exists(pred_json_path): with open(pred_json_path, 'w') as f: f.write('[]') # Load annotations and predictions # For a real run, ensure your JSON files contain actual data. try: coco_gt = COCO(anno_json_path) coco_dt = coco_gt.loadRes(pred_json_path) # Evaluate bounding boxes coco_eval = COCOeval(coco_gt, coco_dt, "bbox") coco_eval.evaluate() coco_eval.accumulate() coco_eval.summarize() print("COCO evaluation (bbox) summarized.") # Option 2: Directly use faster_coco_eval classes (alternative to init_as_pycocotools) from faster_coco_eval import COCO as FasterCOCO, COCOeval_faster # Load annotations and predictions coco_gt_f = FasterCOCO(anno_json_path) coco_dt_f = coco_gt_f.loadRes(pred_json_path) # Evaluate segmentation masks coco_eval_f = COCOeval_faster(coco_gt_f, coco_dt_f, "segm") coco_eval_f.evaluate() coco_eval_f.accumulate() coco_eval_f.summarize() print("Faster COCO evaluation (segm) summarized.") except Exception as e: print(f"An error occurred during COCO evaluation: {e}") print("Please ensure your annotation and prediction JSON files are valid and contain data.") # Clean up dummy files os.remove(anno_json_path) os.remove(pred_json_path)