Reproduce the result of MViT on AVA

Hi, thanks for the code base

I'm trying to reproduce the results on AVA dataset with MViT model. I noticed that while the code is available, the config for finetuning was not provided. I built the config file from the implementation details reported in the paper but only got around 20 mAP. Am I do the right way to reproduce the results?

Any suggestions or discussions are welcome, thank you

```
TRAIN:
ENABLE: True
DATASET: ava
BATCH_SIZE: 128
EVAL_PERIOD: 1
CHECKPOINT_PERIOD: 10
AUTO_RESUME: True
CHECKPOINT_FILE_PATH: checkpoints/MViTv2_S_16x4_k400_f302660347.pyth
CHECKPOINT_TYPE: pytorch
CHECKPOINT_EPOCH_RESET: True
DATA:
NUM_FRAMES: 16
SAMPLING_RATE: 4
TRAIN_JITTER_SCALES: [256, 320]
TRAIN_CROP_SIZE: 224
TEST_CROP_SIZE: 224
INPUT_CHANNEL_NUM: [3]
TRAIN_JITTER_SCALES_RELATIVE: [0.08, 1.0]
TRAIN_JITTER_ASPECT_RELATIVE: [0.75, 1.3333]
DETECTION:
ENABLE: True
ALIGNED: True
AVA:
BGR: False
DETECTION_SCORE_THRESH: 0.8
TEST_PREDICT_BOX_LISTS: ["person_box_67091280_iou90/ava_detection_val_boxes_and_labels.csv"]
FRAME_DIR: "Workspace/dataset/ava/frames/"
FRAME_LIST_DIR: "Workspace/dataset/ava/frame_lists/"
ANNOTATION_DIR: "Workspace/dataset/ava/annotations/"
TRAIN_PREDICT_BOX_LISTS: [
"ava_train_v2.2.csv",
"person_box_67091280_iou90/ava_detection_train_boxes_and_labels_include_negative_v2.2.csv"
]
MVIT:
ZERO_DECAY_POS_CLS: False
USE_ABS_POS: False
SEP_POS_EMBED: True
REL_POS_SPATIAL: True
REL_POS_TEMPORAL: True
DEPTH: 16
NUM_HEADS: 1
EMBED_DIM: 96
PATCH_KERNEL: (3, 7, 7)
PATCH_STRIDE: (2, 4, 4)
PATCH_PADDING: (1, 3, 3)
MLP_RATIO: 4.0
QKV_BIAS: True
DROPPATH_RATE: 0.4
NORM: "layernorm"
MODE: "conv"
CLS_EMBED_ON: False
DIM_MUL: [[1, 2.0], [3, 2.0], [14, 2.0]]
HEAD_MUL: [[1, 2.0], [3, 2.0], [14, 2.0]]
POOL_KVQ_KERNEL: [3, 3, 3]
POOL_KV_STRIDE_ADAPTIVE: [1, 8, 8]
POOL_Q_STRIDE: [[0, 1, 1, 1], [1, 1, 2, 2], [2, 1, 1, 1], [3, 1, 2, 2], [4, 1, 1, 1], [5, 1, 1, 1], [6, 1, 1, 1], [7, 1, 1, 1], [8, 1, 1, 1], [9, 1, 1, 1], [10, 1, 1, 1], [11, 1, 1, 1], [12, 1, 1, 1], [13, 1, 1, 1], [14, 1, 2, 2], [15, 1, 1, 1]]
DROPOUT_RATE: 0.0
DIM_MUL_IN_ATT: True
RESIDUAL_POOLING: True
AUG:
NUM_SAMPLE: 2
ENABLE: True
COLOR_JITTER: 0.4
AA_TYPE: rand-m7-n4-mstd0.5-inc1
INTERPOLATION: bicubic
RE_PROB: 0.25
RE_MODE: pixel
RE_COUNT: 1
RE_SPLIT: False
MIXUP:
ENABLE: False
ALPHA: 0.8
CUTMIX_ALPHA: 1.0
PROB: 1.0
SWITCH_PROB: 0.5
LABEL_SMOOTH_VALUE: 0.1
BN:
USE_PRECISE_STATS: False
NUM_BATCHES_PRECISE: 200
SOLVER:
ZERO_WD_1D_PARAM: True
CLIP_GRAD_L2NORM: 1.0
BASE_LR_SCALE_NUM_SHARDS: True
BASE_LR: 0.6
COSINE_END_LR: 0.06
WARMUP_START_LR: 0.06
WARMUP_EPOCHS: 5.0
LR_POLICY: cosine
MAX_EPOCH: 30
MOMENTUM: 0.9
WEIGHT_DECAY: 1e-8
OPTIMIZING_METHOD: sgd
COSINE_AFTER_WARMUP: True
MODEL:
NUM_CLASSES: 80
ARCH: mvit
MODEL_NAME: MViT
LOSS_FUNC: bce
DROPOUT_RATE: 0.5
HEAD_ACT: sigmoid
TEST:
ENABLE: True
DATASET: ava
BATCH_SIZE: 8
NUM_SPATIAL_CROPS: 1
DATA_LOADER:
NUM_WORKERS: 32
PIN_MEMORY: True
NUM_GPUS: 8
NUM_SHARDS: 1
RNG_SEED: 0
OUTPUT_DIR: Workspace/project/Ava_Transforme/log_out
TENSORBOARD:
ENABLE: True
```