vjxla / configs /eval /vitl /jester.yaml
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nodes: 8
tasks_per_node: 8
cpus_per_task: 12
mem_per_gpu: 200G
tag: jester-vitl16-32x4x3
eval_name: video_classification_frozen
folder: /your_folder/evals/vitl/jester
resume_checkpoint: true
experiment:
classifier:
num_probe_blocks: 4
num_heads: 16
data:
dataset_type: VideoDataset
dataset_train: /your_data_dir/Jester/annotations/jester_train_paths.csv
dataset_val: /your_data_dir/Jester/annotations/jester_validation_paths.csv
num_classes: 27
resolution: 256
frames_per_clip: 32
frame_step: 2
num_segments: 4
num_views_per_segment: 3
optimization:
use_pos_embed: false
num_epochs: 100
batch_size: 2
use_bfloat16: true
multihead_kwargs:
- weight_decay: 0.8
final_weight_decay: 0.8
lr: 0.001
start_lr: 0.001
final_lr: 0.0
warmup: 0.
- weight_decay: 0.8
final_weight_decay: 0.8
lr: 0.0003
start_lr: 0.0003
final_lr: 0.0
warmup: 0.
- weight_decay: 0.8
final_weight_decay: 0.8
lr: 0.0001
start_lr: 0.0001
final_lr: 0.0
warmup: 0.
model_kwargs:
checkpoint: /your_vjepa2_checkpoints/vitl.pt
module_name: evals.video_classification_frozen.modelcustom.vit_encoder_multiclip_multilevel
wrapper_kwargs:
max_frames: 128
use_pos_embed: false
out_layers: [17, 19, 21, 23]
pretrain_kwargs:
encoder:
model_name: vit_large
checkpoint_key: target_encoder
tubelet_size: 2
patch_size: 16
uniform_power: true
use_rope: true