MODEL: META_ARCHITECTURE: "GeneralizedVLRCNN" WEIGHT: "swin_large_patch4_window12_384_22k.pth" RPN_ONLY: True RPN_ARCHITECTURE: "VLDYHEAD" BACKBONE: CONV_BODY: "SWINT-FPN-RETINANET" OUT_CHANNELS: 256 SWINT: EMBED_DIM: 192 DEPTHS: (2, 2, 18, 2) NUM_HEADS: (6, 12, 24, 48) WINDOW_SIZE: 12 OUT_CHANNELS: (192, 384, 768, 1536) DROP_PATH_RATE: 0.4 LANGUAGE_BACKBONE: FREEZE: False MODEL_TYPE: "bert-base-uncased" # "roberta-base", "clip" MASK_SPECIAL: False RPN: USE_FPN: True ANCHOR_SIZES: (64, 128, 256, 512, 1024) ANCHOR_STRIDE: (8, 16, 32, 64, 128) ASPECT_RATIOS: (1.0,) SCALES_PER_OCTAVE: 1 DYHEAD: CHANNELS: 256 NUM_CONVS: 8 USE_GN: True USE_DYRELU: True USE_DFCONV: True USE_DYFUSE: True TOPK: 9 # topk for selecting candidate positive samples from each level SCORE_AGG: "MEAN" LOG_SCALE: 0.0 USE_CHECKPOINT: True FUSE_CONFIG: USE_FUSED_FEATURES_DOT_PRODUCT: True EARLY_FUSE_ON: True TYPE: "MHA-B" USE_CLASSIFICATION_LOSS: False USE_TOKEN_LOSS: False USE_CONTRASTIVE_ALIGN_LOSS: False CONTRASTIVE_HIDDEN_DIM: 64 USE_DOT_PRODUCT_TOKEN_LOSS: True USE_LAYER_SCALE: True CLAMP_MIN_FOR_UNDERFLOW: True CLAMP_MAX_FOR_OVERFLOW: True CLAMP_BERTATTN_MIN_FOR_UNDERFLOW: True CLAMP_BERTATTN_MAX_FOR_OVERFLOW: True CLAMP_DOT_PRODUCT: True DATASETS: TRAIN: ("mixed_train_no_coco",) # Place holder dataset for now. To be updated in the next version TEST: ("coco_2017_val", ) ONE_HOT: False FLICKR_COPY: 8 # 0.15 * 8 = ~1.2M MIXED_COPY: 4 # 0.6 * 4 = ~2.4M OBJECT365_COPY: 2 # 1.4 * 2 = ~2.8M VG_COPY: 3 # 0.4 * 3 = ~1.2M IN_COPY: 2 # 0.67 * 2 = ~1.33M OI_COPY: 1 # 2M * 1 = 2M DISABLE_SHUFFLE: False ADD_DET_PROMPT: False RANDOM_SAMPLE_NEG: 85 CONTROL_PROB: (0.0, 0.0, 0.5, 0.0) FURTHER_SCREEN: True CAPTION_CONF: 0.5 CAPTION_NMS: -1.0 CAPTION_MIN_BOX: 1 SEPARATION_TOKENS: ". " PACK_RANDOM_CAPTION_NUMBER: 20 NO_RANDOM_PACK_PROBABILITY: 0.4 RANDOM_PACK_PROB: 0.5 CAPTION_FORMAT_VERSION: "v2" INPUT: PIXEL_MEAN: [ 103.530, 116.280, 123.675 ] PIXEL_STD: [ 57.375, 57.120, 58.395 ] MIN_SIZE_TRAIN: 800 MAX_SIZE_TRAIN: 1333 MIN_SIZE_TEST: 800 MAX_SIZE_TEST: 1333 AUGMENT: MULT_MIN_SIZE_TRAIN: (480,560,640,720,800) DATALOADER: SIZE_DIVISIBILITY: 32 SOLVER: OPTIMIZER: ADAMW BASE_LR: 0.0001 LANG_LR: 0.00001 WEIGHT_DECAY: 0.01 WEIGHT_DECAY_SCHEDULE: True STEPS: (0.67, 0.89) MAX_ITER: 1000000 IMS_PER_BATCH: 64 WARMUP_ITERS: 2000 WARMUP_FACTOR: 0.001 FIND_UNUSED_PARAMETERS: False CLIP_GRADIENTS: ENABLED: True CLIP_TYPE: "full_model" CLIP_VALUE: 1.0 NORM_TYPE: 2.0