Upload faster_rcnn_resnet101_1xcoco-default-mmdetection-config.py

# Config - Base MMDetection config
- for usage in the app [IllegalDumpSiteDetectionAndLandfillMonitoring.](https://github.com/IntelligentNetworkSolutions/IllegalDumpSiteDetectionAndLandfillMonitoring.)
- variables for num_batch_size, num_epochs, num_frozen_stages

# Model Weight
- downloaded from:
- page:
[MMDetection Faster RCNN Model Zoo](https://github.com/open-mmlab/mmdetection/tree/main/configs/faster_rcnn)
- specifically:
[MMDetection Trained Faster RCNN on ResNet 101 with COCO - Model File](https://download.openxlab.org.cn/models/mmdetection/FasterR-CNN/weight/faster-rcnn_r101_fpn_1x_coco)

faster_rcnn_resnet101_1xcoco-default-mmdetection-config.py

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+num_batch_size = 2
+num_epochs = 12
+num_frozen_stages = 1
+
+# DATASET
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+
+backend_args = None
+
+train_pipeline = [
+    dict(type='LoadImageFromFile', backend_args=backend_args),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='Resize', scale=(1280, 1280), keep_ratio=True),
+    dict(type='RandomFlip', prob=0.5),
+    dict(type='PackDetInputs')
+]
+train_dataloader = dict(
+    batch_size=num_batch_size,
+    num_workers=2,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    batch_sampler=dict(type='AspectRatioBatchSampler'),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='train/annotations_coco.json',
+        data_prefix=dict(img='train/'),
+        filter_cfg=dict(filter_empty_gt=True, min_size=32),
+        pipeline=train_pipeline,
+        backend_args=backend_args))
+
+val_pipeline = [
+    dict(type='LoadImageFromFile', backend_args=backend_args),
+    dict(type='Resize', scale=(1280, 1280), keep_ratio=True),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor'))
+]
+val_dataloader = dict(
+    batch_size=num_batch_size,
+    num_workers=2,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='valid/annotations_coco.json',
+        data_prefix=dict(img='valid/'),
+        test_mode=True,
+        pipeline=val_pipeline,
+        backend_args=backend_args))
+val_evaluator = dict(
+    type='CocoMetric',
+    ann_file=data_root + 'valid/annotations_coco.json',
+    metric='bbox',
+    format_only=False,
+    backend_args=backend_args)
+
+test_pipeline = [
+    dict(type='LoadImageFromFile', backend_args=backend_args),
+    dict(type='Resize', scale=(1280, 1280), keep_ratio=True),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor'))
+]
+test_dataloader = dict(
+    batch_size=num_batch_size,
+    num_workers=2,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=data_root + 'test/annotations_coco.json',
+        data_prefix=dict(img='test/'),
+        test_mode=True,
+        pipeline=test_pipeline))
+test_evaluator = dict(
+    type='CocoMetric',
+    metric='bbox',
+    format_only=True,
+    ann_file=data_root + 'test/annotations_coco.json',
+    outfile_prefix='./work_dirs/coco_detection/test')
+
+
+# MODEL
+model = dict(
+    type='FasterRCNN',
+    data_preprocessor=dict(
+        type='DetDataPreprocessor',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        bgr_to_rgb=True,
+        pad_size_divisor=32),
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=num_frozen_stages,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='https://download.openxlab.org.cn/models/mmdetection/FasterR-CNN/weight/faster-rcnn_r101_fpn_1x_coco')),
+    neck=dict(type='FPN', in_channels=[256, 512, 1024, 2048], out_channels=256, num_outs=5),
+    rpn_head=dict(
+        type='RPNHead',
+        in_channels=256, feat_channels=256,
+        anchor_generator=dict(type='AnchorGenerator', scales=[8], ratios=[0.5, 1.0, 2.0], strides=[4, 8, 16, 32, 64]),
+        bbox_coder=dict(type='DeltaXYWHBBoxCoder', target_means=[.0, .0, .0, .0], target_stds=[1.0, 1.0, 1.0, 1.0]),
+        loss_cls=dict(type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
+    roi_head=dict(
+        type='StandardRoIHead',
+        bbox_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
+            out_channels=256, featmap_strides=[4, 8, 16, 32]),
+        bbox_head=dict(
+            type='Shared2FCBBoxHead',
+            in_channels=256,
+            fc_out_channels=1024,
+            roi_feat_size=7,
+            num_classes=80,
+            bbox_coder=dict(type='DeltaXYWHBBoxCoder', target_means=[0., 0., 0., 0.], target_stds=[0.1, 0.1, 0.2, 0.2]),
+            reg_class_agnostic=False,
+            loss_cls=dict(type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
+            loss_bbox=dict(type='L1Loss', loss_weight=1.0))),
+    # model training and testing settings
+    train_cfg=dict(
+        rpn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.7, neg_iou_thr=0.3, min_pos_iou=0.3, 
+                match_low_quality=True, ignore_iof_thr=-1),
+            sampler=dict(type='RandomSampler', num=256, pos_fraction=0.5, neg_pos_ub=-1, add_gt_as_proposals=False),
+            allowed_border=-1, pos_weight=-1, debug=False),
+        rpn_proposal=dict(nms_pre=2000, max_per_img=1000, nms=dict(type='nms', iou_threshold=0.7), min_bbox_size=0),
+        rcnn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.5, neg_iou_thr=0.5, min_pos_iou=0.5, 
+                match_low_quality=False, ignore_iof_thr=-1),
+            sampler=dict(type='RandomSampler', num=512, pos_fraction=0.25, neg_pos_ub=-1, add_gt_as_proposals=True),
+            pos_weight=-1,
+            debug=False)),
+    test_cfg=dict(
+        rpn=dict(nms_pre=1000, max_per_img=1000, nms=dict(type='nms', iou_threshold=0.7), min_bbox_size=0),
+        rcnn=dict(score_thr=0.05, nms=dict(type='nms', iou_threshold=0.5), max_per_img=100)
+    ))
+
+# RUNTIME
+default_scope = 'mmdet'
+
+default_hooks = dict(
+    timer=dict(type='IterTimerHook'),
+    logger=dict(type='LoggerHook', interval=50),
+    param_scheduler=dict(type='ParamSchedulerHook'),
+    checkpoint=dict(type='CheckpointHook', interval=1),
+    sampler_seed=dict(type='DistSamplerSeedHook'),
+    visualization=dict(type='DetVisualizationHook'))
+
+env_cfg = dict(
+    cudnn_benchmark=False,
+    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
+    dist_cfg=dict(backend='nccl'),
+)
+
+vis_backends = [dict(type='LocalVisBackend')]
+visualizer = dict(type='DetLocalVisualizer', vis_backends=vis_backends, name='visualizer')
+log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True)
+
+log_level = 'INFO'
+load_from = None
+resume = False
+
+# SCHEDULE
+# training schedule for 1x
+train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=num_epochs, val_interval=1)
+val_cfg = dict(type='ValLoop')
+test_cfg = dict(type='TestLoop')
+
+# learning rate
+param_scheduler = [
+    dict(type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
+    dict(type='MultiStepLR', begin=0, end=12, by_epoch=True, milestones=[8, 11], gamma=0.1)
+]
+
+# optimizer
+optim_wrapper = dict(type='OptimWrapper', optimizer=dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001))
+
+auto_scale_lr = dict(enable=False, base_batch_size=16)