GraphAttributeLearning / configs /experiment.yaml
Ashish Mehta
Add initial project structure and configuration files for Adjective-Aware Object Embedding
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experiment:
id: exp_clip_dino_gnn_v1
description: CLIP+DINO fusion with bipartite GNN and BCE+InfoNCE.
tags:
- baseline_c_plus_gnn
- adjective_embedding
- visual_genome
defaults:
dataset: configs/dataset.yaml
model: configs/model.yaml
train: configs/train.yaml
eval: configs/eval.yaml
baseline_runs:
- name: baseline_a_clip_only_mlp
overrides:
encoders.clip.enabled: true
encoders.dino.enabled: false
graph.variant: none
gnn.type: none
- name: baseline_b_dino_only_mlp
overrides:
encoders.clip.enabled: false
encoders.dino.enabled: true
graph.variant: none
gnn.type: none
- name: baseline_c_clip_dino_mlp
overrides:
encoders.clip.enabled: true
encoders.dino.enabled: true
graph.variant: none
gnn.type: none
main_run:
name: proposed_clip_dino_gnn
overrides:
graph.variant: bipartite
gnn.type: gat
attribute_nodes.init_type: clip_text
ablation_runs:
- name: graph_enriched_with_attribute_priors
overrides:
graph.variant: enriched
graph.include_attribute_priors: true
- name: trainable_attribute_nodes
overrides:
attribute_nodes.init_type: trainable
- name: bce_only
overrides:
loss.contrastive.enabled: false
- name: partial_encoder_finetune
overrides:
training.stage2.enabled: true
training.stage2.unfreeze_top_n_layers: 4