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Issue 323 - Changes made in preprocessing images for torch detection models. #325

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Merged
merged 3 commits into from
Nov 5, 2025
Merged

Issue 323 - Changes made in preprocessing images for torch detection models. #325

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e67166f
Added default resize
SakhinetiPraveena Oct 6, 2025
fed553c
Added image resize height and width as input parameters in streamlit UI
SakhinetiPraveena Oct 30, 2025
ada0a05
Merge branch 'master' into issue-323
SakhinetiPraveena Oct 30, 2025
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29 changes: 22 additions & 7 deletions app.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -187,7 +187,6 @@ def browse_folder():
"Confidence Threshold",
min_value=0.0,
max_value=1.0,
value=st.session_state.get("confidence_threshold", 0.5),
step=0.01,
key="confidence_threshold",
help="Minimum confidence score for detections",
Expand All @@ -196,7 +195,6 @@ def browse_folder():
"NMS Threshold",
min_value=0.0,
max_value=1.0,
value=st.session_state.get("nms_threshold", 0.5),
step=0.01,
key="nms_threshold",
help="Non-maximum suppression threshold",
Expand All @@ -205,15 +203,22 @@ def browse_folder():
"Max Detections/Image",
min_value=1,
max_value=1000,
value=st.session_state.get("max_detections", 100),
step=1,
key="max_detections",
)
st.number_input(
"Image Resize Height",
min_value=1,
max_value=4096,
value=640,
step=1,
key="resize_height",
help="Height to resize images for inference",
)
with col2:
st.selectbox(
"Device",
["cpu", "cuda", "mps"],
index=0 if st.session_state.get("device", "cpu") == "cpu" else 1,
key="device",
)
st.selectbox(
Expand All @@ -231,20 +236,26 @@ def browse_folder():
"Batch Size",
min_value=1,
max_value=256,
value=st.session_state.get("batch_size", 1),
step=1,
key="batch_size",
)
st.number_input(
"Evaluation Step",
min_value=0,
max_value=1000,
value=st.session_state.get("evaluation_step", 10),
step=1,
key="evaluation_step",
help="Update UI with intermediate metrics every N images (0 = disable intermediate updates)",
)

st.number_input(
"Image Resize Width",
min_value=1,
max_value=4096,
value=640,
step=1,
key="resize_width",
help="Width to resize images for inference",
)
# Load model action in sidebar
from detectionmetrics.models.torch_detection import TorchImageDetectionModel
import json, tempfile
Expand Down Expand Up @@ -292,6 +303,8 @@ def browse_folder():
device = st.session_state.get("device", "cpu")
batch_size = int(st.session_state.get("batch_size", 1))
evaluation_step = int(st.session_state.get("evaluation_step", 5))
resize_height = int(st.session_state.get("resize_height", 640))
resize_width = int(st.session_state.get("resize_width", 640))
model_format = st.session_state.get("model_format", "torchvision")
config_data = {
"confidence_threshold": confidence_threshold,
Expand All @@ -300,6 +313,8 @@ def browse_folder():
"device": device,
"batch_size": batch_size,
"evaluation_step": evaluation_step,
"resize_height": resize_height,
"resize_width": resize_width,
"model_format": model_format.lower(),
}
with tempfile.NamedTemporaryFile(
Expand Down
23 changes: 14 additions & 9 deletions detectionmetrics/models/torch_detection.py
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Original file line number Diff line number Diff line change
Expand Up @@ -260,16 +260,21 @@ def __init__(
# Build input transforms (resize, normalize, etc.)
self.transform_input = []

# Default resize to 640x640 if not specified
if "resize" in self.model_cfg:
self.transform_input += [
transforms.Resize(
size=(
self.model_cfg["resize"].get("height", None),
self.model_cfg["resize"].get("width", None),
),
interpolation=transforms.InterpolationMode.BILINEAR,
)
]
resize_height = self.model_cfg["resize"].get("height", 640)
resize_width = self.model_cfg["resize"].get("width", 640)
else:
# Default to 640x640 when no resize is specified
resize_height = 640
resize_width = 640

self.transform_input += [
transforms.Resize(
size=(resize_height, resize_width),
interpolation=transforms.InterpolationMode.BILINEAR,
)
]

if "crop" in self.model_cfg:
crop_size = (
Expand Down