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checkpoint_best_total.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:98f3d25a38ae7f9e598d02bead7903179ed99e8114ab99a31dfe9a81b69f532e
+size 127634110

rf-detr-base.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d8f70210e425a4a4234d547737f57500bcc4ac24a333b99e33d9d5a371e0b80f
+size 372578043

script.py

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+import os
+import torch
+import pandas as pd
+from rfdetr import RFDETRBase
+
+
+def run_inference(model, image_path, conf_threshold, save_path):
+
+    test_images = sorted([
+        f for f in os.listdir(image_path)
+        if f.lower().endswith((".jpg", ".jpeg", ".png"))
+    ])
+
+    bboxes = []
+    category_ids = []
+    test_images_names = []
+
+    for image_name in test_images:
+        test_images_names.append(image_name)
+
+        image_file = os.path.join(image_path, image_name)
+
+        bbox = []
+        category_id = []
+
+        preds = model.predict(image_file)
+
+        if preds is not None and preds.xyxy is not None and len(preds.xyxy) > 0:
+            for box, score, label in zip(
+                preds.xyxy,
+                preds.confidence,
+                preds.class_id
+            ):
+                score = float(score)
+                if score >= conf_threshold:
+                    xmin, ymin, xmax, ymax = map(float, box)
+
+                    width = xmax - xmin
+                    height = ymax - ymin
+
+                    bbox.append([xmin, ymin, width, height])
+                    category_id.append(int(label))
+
+        bboxes.append(bbox)
+        category_ids.append(category_id)
+
+    df_predictions = pd.DataFrame(columns=["file_name", "bbox", "category_id"])
+
+    for i in range(len(test_images_names)):
+        new_row = pd.DataFrame({
+            "file_name": test_images_names[i],
+            "bbox": str(bboxes[i]),
+            "category_id": str(category_ids[i])
+        }, index=[0])
+
+        df_predictions = pd.concat([df_predictions, new_row], ignore_index=True)
+
+    df_predictions.to_csv(save_path, index=False)
+
+
+if __name__ == "__main__":
+
+    TEST_IMAGE_PATH = r"rf-detr\dataset\test"
+    SUBMISSION_SAVE_PATH = "submission.csv"
+    CONF_THRESHOLD = 0.30
+
+    model = RFDETRBase(
+        checkpoint_path="checkpoint_best_total.pth",
+        device="cuda" if torch.cuda.is_available() else "cpu"
+    )
+
+    run_inference(model, TEST_IMAGE_PATH, CONF_THRESHOLD, SUBMISSION_SAVE_PATH)