update app file

app.py

@@ -81,7 +81,7 @@ with gr.Blocks(css='style.css') as demo:
                                           label='Model Type')
                 with gr.Row():
                     model_name = gr.Dropdown(([
-                        'Faster R-CNN (R-50-FPN']),
+                        'Faster R-CNN (R-50-FPN)']),
                                              value=DEFAULT_MODEL_NAME,
                                              label='Model')
             with gr.Row():

configs/_base_/faster-rcnn_r50_fpn_1x_coco.py

@@ -166,4 +166,73 @@ test_pipeline = [
         ])
 ]
 
-evaluation = dict(interval=1, metric='bbox')
+evaluation = dict(interval=1, metric='bbox')
+
+data_root = 'data/' # dataset root
+
+train_batch_size_per_gpu = 16
+train_num_workers = 1
+
+max_epochs = 105
+base_lr = 0.00001
+
+
+metainfo = {
+    'classes': ('orgaquant', ),
+    'palette': [
+        (220, 20, 60),
+    ]
+}
+
+train_dataloader = dict(
+    batch_size=train_batch_size_per_gpu,
+    num_workers=train_num_workers,
+    dataset=dict(
+        data_root=data_root,
+        metainfo=metainfo,
+        data_prefix=dict(img='train/'),
+        ann_file='train.json'))
+
+val_dataloader = dict(
+    dataset=dict(
+        data_root=data_root,
+        metainfo=metainfo,
+        data_prefix=dict(img='val/'),
+        ann_file='val.json'))
+
+test_dataloader = val_dataloader
+
+val_evaluator = dict(ann_file=data_root + 'val.json')
+
+test_evaluator = val_evaluator
+
+model = dict(
+    roi_head=dict(
+        bbox_head=dict(num_classes=1)))
+
+    
+    
+# optimizer
+optim_wrapper = dict(
+    _delete_=True,
+    type='OptimWrapper',
+    optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.05),
+    paramwise_cfg=dict(
+        norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True))
+
+default_hooks = dict(
+    checkpoint=dict(
+        interval=5,
+        max_keep_ckpts=2,  # only keep latest 2 checkpoints
+        save_best='auto'
+    ),
+    logger=dict(type='LoggerHook', interval=5))
+
+
+# load COCO pre-trained weight
+
+# load_from = './work_dirs/faster-rcnn_r50_fpn_organoid/best_coco_bbox_mAP_epoch_12.pth'
+
+
+train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=1)
+visualizer = dict(vis_backends=[dict(type='LocalVisBackend'),dict(type='TensorboardVisBackend')])

configs/{faster-rcnn_r50_fpn_organoid_orgaquant.py → _base_/faster-rcnn_r50_fpn_organoid_orgaquant.py}

@@ -2,7 +2,7 @@
 # Inherit and overwrite part of the config based on this config
 _base_ = './faster-rcnn_r50_fpn_1x_coco.py'
 
-data_root = 'data/' # dataset root
+data_root = 'orgaquant_dataset/' # dataset root
 
 train_batch_size_per_gpu = 16
 train_num_workers = 1

model.py

@@ -23,7 +23,7 @@ class Model:
 
 
     def _load_model(self, name: str) -> nn.Module:
-        return init_detector('configs/_base_/faster-rcnn_r50_fpn_1x_coco.py', 'models/orgaquant_pretrained.pth' , device=self.device)
+        return init_detector('configs/_base_/faster-rcnn_r50_organoid_orgaquant.py', 'models/orgaquant_pretrained.pth' , device=self.device)
 
     def set_model(self, name: str) -> None:
         if name == self.model_name: