fix: add model

app.py

@@ -3,9 +3,8 @@ import gradio as gr
 import torch
 import logging
 import torchvision
-from torchvision.models.detection.faster_rcnn import fasterrcnn_resnet50_fpn_v2
+from torchvision.models.detection.faster_rcnn import fasterrcnn_resnet50_fpn
 from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
-from configs.path_cfg import OUTPUT_DIR
 from src.detection.graph_utils import add_bbox
 from src.detection.vision import presets
 logging.getLogger('PIL').setLevel(logging.CRITICAL)
@@ -13,48 +12,50 @@ logging.getLogger('PIL').setLevel(logging.CRITICAL)
 
 def load_model(baseline: bool = False):
     if baseline:
-        model = fasterrcnn_resnet50_fpn_v2(
+        model = fasterrcnn_resnet50_fpn(
             weights="DEFAULT")
     else:
-        model = fasterrcnn_resnet50_fpn_v2()
+        model = fasterrcnn_resnet50_fpn()
         in_features = model.roi_heads.box_predictor.cls_score.in_features
         model.roi_heads.box_predictor = FastRCNNPredictor(in_features, 2)
-        checkpoint = torch.load(osp.join(OUTPUT_DIR, "detection_logs",
-                                "fasterrcnn_training", "checkpoint.pth"), map_location="cpu")
+        checkpoint = torch.load(
+            "model_split_3_FT_MOT17.pth", map_location="cpu")
         model.load_state_dict(checkpoint["model"])
+    device = torch.device('cuda:0')
+    model.to(device)
     model.eval()
     return model
 
 
-def detect_with_resnet50Model_finetuning_motsynth(image):
+def frcnn_motsynth(image):
     model = load_model(baseline=True)
     transformEval = presets.DetectionPresetEval()
     image_tensor = transformEval(image, None)[0]
     prediction = model([image_tensor])[0]
-    image_w_bbox = add_bbox(image_tensor, prediction, 0.85)
+    image_w_bbox = add_bbox(image_tensor, prediction, 0.80)
     torchvision.io.write_png(image_w_bbox, "custom_out.png")
     return "custom_out.png"
 
 
-def detect_with_resnet50Model_baseline(image):
+def frcnn_coco(image):
     model = load_model(baseline=True)
     transformEval = presets.DetectionPresetEval()
     image_tensor = transformEval(image, None)[0]
     prediction = model([image_tensor])[0]
-    image_w_bbox = add_bbox(image_tensor, prediction, 0.85)
+    image_w_bbox = add_bbox(image_tensor, prediction, 0.80)
     torchvision.io.write_png(image_w_bbox, "baseline_out.png")
     return "baseline_out.png"
 
 
-title = "Performance comparision of Faster R-CNN for people detection with syntetic data"
-description = "<p style='text-align: center'>Performance comparision of Faster RCNN models for people detection using MOTSynth and MOT17"
+title = "Domain shift adaption on pedestrian detection with Faster R-CNN"
+description = "![alt text](http://www.aiacademy.unimore.it/media/news/ai-logo-white_2ND_EDITION.png)"
 examples = "/input_examples"
 
-io_baseline = gr.Interface(detect_with_resnet50Model_baseline, gr.Image(type="pil"), gr.Image(
-    type="file", shape=(1920, 1080), label="Baseline Faster RCNN Model pretrained on COCO dataset"))
+io_baseline = gr.Interface(frcnn_coco, gr.Image(type="pil"), gr.Image(
+    type="file", shape=(1920, 1080), label="Baseline Model trained on COCO + FT on MOT17"))
 
-io_custom = gr.Interface(detect_with_resnet50Model_finetuning_motsynth, gr.Image(type="pil"), gr.Image(
-    type="file", shape=(1920, 1080), label="Faster RCNN Model pretrained on COCO dataset + FT on MOTSynth"))
+io_custom = gr.Interface(frcnn_motsynth, gr.Image(type="pil"), gr.Image(
+    type="file", shape=(1920, 1080), label="Faster R-CNN trained on MOTSynth + FT on MOT17"))
 
 gr.Parallel(io_baseline, io_custom, title=title,
             description=description, examples=examples).launch(enable_queue=True)

configs/path_cfg.py

@@ -1,19 +0,0 @@
-import os
-import sys
-import os
-
-IN_COLAB = False
-if 'COLAB_GPU' in os.environ:
-   IN_COLAB=True
-
-cwd = os.getcwd()
-
-if(IN_COLAB):
-    MOTSYNTH_ROOT = '/content/gdrive/MyDrive/CVCS/storage/MOTSynth'
-    MOTCHA_ROOT = '/content/gdrive/MyDrive/CVCS/storage/MOTChallenge'
-    OUTPUT_DIR = '/content/gdrive/MyDrive/CVCS/storage/motsynth_output'
-else:
-    # windows config
-    MOTSYNTH_ROOT = cwd + '\storage\MOTSynth'
-    MOTCHA_ROOT = cwd + '\storage\MOTChallenge'
-    OUTPUT_DIR = cwd + '\storage\motsynth_output'

model_split3_FT_MOT17.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:53116b936ee59ca7cd9f29ef99bc8bf1dc591b6e8955f6c380b083454535923d
+size 330056867