Upload grounding_dino_swin-t_finetune_8xb2_20e_concrete.py with huggingface_hub

grounding_dino_swin-t_finetune_8xb2_20e_concrete.py

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+# _base_ = 'grounding_dino_swin-t_finetune_16xb2_1x_coco.py'
+_base_ = './grounding_dino_swin-t_finetune_16xb2_1x_coco.py'
+
+data_root = 'data/concrete_defect/'
+class_name = ('crack', 'corrosion', 'efflorescence', 'pothole', 'spalling')
+num_classes = len(class_name)
+metainfo = dict(classes=class_name,
+                palette=[(244, 108, 59), (99, 102, 129), (249, 193, 0), (160, 180, 0), (115, 82, 59)])
+
+model = dict(bbox_head=dict(num_classes=num_classes))
+
+train_dataloader = dict(
+    dataset=dict(
+        data_root=data_root,
+        metainfo=metainfo,
+        ann_file='annotations/trainval.json',
+        data_prefix=dict(img='images/')))
+
+val_dataloader = dict(
+    dataset=dict(
+        metainfo=metainfo,
+        data_root=data_root,
+        ann_file='annotations/test.json',
+        data_prefix=dict(img='images/')))
+
+test_dataloader = val_dataloader
+
+val_evaluator = dict(ann_file=data_root + 'annotations/test.json')
+test_evaluator = val_evaluator
+
+max_epoch = 20
+
+default_hooks = dict(
+    checkpoint=dict(interval=1, max_keep_ckpts=1, save_best='auto'),
+    logger=dict(type='LoggerHook', interval=5))
+train_cfg = dict(max_epochs=max_epoch, val_interval=1)
+
+param_scheduler = [
+    dict(type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=30),
+    dict(
+        type='MultiStepLR',
+        begin=0,
+        end=max_epoch,
+        by_epoch=True,
+        milestones=[15],
+        gamma=0.1)
+]
+
+optim_wrapper = dict(
+    optimizer=dict(lr=0.00005),
+    paramwise_cfg=dict(
+        custom_keys={
+            'absolute_pos_embed': dict(decay_mult=0.),
+            'backbone': dict(lr_mult=0.1),
+            'language_model': dict(lr_mult=0),
+        }))
+
+auto_scale_lr = dict(base_batch_size=16)
+# auto_scale_lr = dict(base_batch_size=4)