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Increase number of detections FasterRCNN will return #95

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Merged
mihow merged 2 commits into from
Nov 4, 2025
Merged

Increase number of detections FasterRCNN will return #95

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6c0d6bf
fix: increase number of detections FasterRCNN will return
mihow Nov 4, 2025
5092553
fix: set limit to something more reasonable
mihow Nov 4, 2025
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4 changes: 2 additions & 2 deletions trapdata/api/api.py
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Original file line number Diff line number Diff line change
Expand Up @@ -295,8 +295,8 @@ async def process(data: PipelineRequest) -> PipelineResponse:
logger.info(f"Returning {len(detections_to_return)} detections")
# print(all_detections)

# If the number of detections is greater than 100, its suspicious. Log it.
if len(detections_to_return) > 100:
# If the number of detections is greater than 200, its suspicious. Log it.
if len(detections_to_return) > 200:
logger.warning(
f"Detected {len(detections_to_return)} detections. "
"This is suspicious and may contain duplicates."
Expand Down
11 changes: 9 additions & 2 deletions trapdata/ml/models/localization.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,5 @@
import pathlib

import PIL.Image
import torch
import torchvision
import torchvision.models.detection.anchor_utils
Expand Down Expand Up @@ -206,9 +205,13 @@ class MothObjectDetector_FasterRCNN_2021(ObjectDetector):
"Accurate but can be slow on a machine without GPU."
)
bbox_score_threshold = 0.99
box_detections_per_img = 500

def get_model(self):
model = torchvision.models.detection.fasterrcnn_resnet50_fpn(weights=None)
model = torchvision.models.detection.fasterrcnn_resnet50_fpn(
weights=None,
box_detections_per_img=self.box_detections_per_img,
)
num_classes = 2 # 1 class (object) + background
in_features = model.roi_heads.box_predictor.cls_score.in_features
model.roi_heads.box_predictor = (
Expand Down Expand Up @@ -249,6 +252,7 @@ class MothObjectDetector_FasterRCNN_2023(ObjectDetector):
"Accurate but can be slow on a machine without GPU."
)
bbox_score_threshold = 0.80
box_detections_per_img = 500

def get_model(self):
num_classes = 2 # 1 class (object) + background
Expand All @@ -257,6 +261,7 @@ def get_model(self):
name="fasterrcnn_resnet50_fpn",
num_classes=num_classes,
weights=None,
box_detections_per_img=self.box_detections_per_img,
)
checkpoint = torch.load(self.weights, map_location=self.device)
state_dict = checkpoint.get("model_state_dict") or checkpoint
Expand Down Expand Up @@ -293,6 +298,7 @@ class MothObjectDetector_FasterRCNN_MobileNet_2023(ObjectDetector):
trainable_backbone_layers = 6 # all layers are trained
anchor_sizes = (64, 128, 256, 512)
num_classes = 2
box_detections_per_img = 500

def get_model(self):
norm_layer = torch.nn.BatchNorm2d
Expand All @@ -311,6 +317,7 @@ def get_model(self):
anchor_sizes, aspect_ratios
),
rpn_score_thresh=0.05,
box_detections_per_img=self.box_detections_per_img,
)
checkpoint = torch.load(self.weights, map_location=self.device)
state_dict = checkpoint.get("model_state_dict") or checkpoint
Expand Down
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