feat: add path baseline

app.py

@@ -12,20 +12,20 @@ import torchvision.transforms as T
 
 logging.getLogger('PIL').setLevel(logging.CRITICAL)
 
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+device = torch.device("cpu")
 
 
 def load_model(baseline: bool = False):
     if baseline:
-        model = fasterrcnn_resnet50_fpn(
-            weights="DEFAULT")
+        path = osp.join(os.getcwd(), "model_baseline_coco_FT_MOT17.pth")
     else:
-        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(os.getcwd(), "model_split3_FT_MOT17.pth"), map_location="cpu")
-        model.load_state_dict(checkpoint["model"])
+        path = osp.join(os.getcwd(), "model_split3_FT_MOT17.pth")
+
+    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(path, map_location="cpu")
+    model.load_state_dict(checkpoint["model"])
     model.to(device)
     model.eval()
     return model
@@ -56,7 +56,7 @@ def frcnn_coco(image):
 title = "Domain shift adaption on pedestrian detection with Faster R-CNN"
 description = '<p style="text-align:center">School in AI: Deep Learning, Vision and Language for Industry - second edition final project work by Matteo Sirri.</p> '
 examples = ["001.jpg", "002.jpg", "003.jpg",
-            "004.jpg", "005.jpg", "006.jpg", "007.jpg", ]
+            "004.jpg", "005.jpg", "006.jpg", "007.jpg"]
 
 io_baseline = gr.Interface(frcnn_coco, gr.Image(type="pil"), gr.Image(
     type="file", label="Baseline Model trained on COCO + FT on MOT17"))

model_baseline_coco_FT_MOT17.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b7647a5e485632e739efd6444b743d191c5f32916a05988c4a6646c40d3cce47
+size 330056483