Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

[mieb] mieb scripts (siglip rerun & linear probing ablation & params count) #1429

Merged
merged 2 commits into from
Nov 10, 2024
Merged
Show file tree
Hide file tree
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
28 changes: 28 additions & 0 deletions scripts/run_mieb_get_params.py
Original file line number Diff line number Diff line change
@@ -0,0 +1,28 @@
from __future__ import annotations

import pandas as pd
import torch
from tqdm import tqdm

import mteb

params = []

# add all model names
Copy link
Collaborator

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Did you mean to populate this list?

model_names = [ # "google/siglip-base-patch16-512",
"google/siglip-so400m-patch14-384"
# ...
]

for model_name in tqdm(model_names):
model = mteb.get_model(model_name)

total_params = sum(p.numel() for p in model.model.parameters())
total_params = total_params / 1e6
params.append([model_name, total_params])

del model
torch.cuda.empty_cache()

param_frame = pd.DataFrame(params, columns=["model name", "# params"])
param_frame.to_csv("params.csv", index=False)
71 changes: 71 additions & 0 deletions scripts/run_mieb_kshot_ablation.py
Original file line number Diff line number Diff line change
@@ -0,0 +1,71 @@
from __future__ import annotations

import mteb

for model_name in [
# key ones for this ablation (different types of models)
"openai/clip-vit-base-patch32",
"openai/clip-vit-base-patch16",
"openai/clip-vit-large-patch14",
"royokong/e5-v",
"facebook/dinov2-small",
"facebook/dinov2-base",
"facebook/dinov2-large",
"facebook/dinov2-giant",
# more insights
"BAAI/bge-visualized-base",
"BAAI/bge-visualized-m3",
"google/siglip-so400m-patch14-384",
"google/siglip-base-patch16-256-multilingual",
"google/siglip-base-patch16-256",
"google/siglip-base-patch16-512",
"google/siglip-base-patch16-384",
"google/siglip-base-patch16-224",
"google/siglip-large-patch16-256",
"google/siglip-large-patch16-384",
"nyu-visionx/moco-v3-vit-b",
"nyu-visionx/moco-v3-vit-l",
"laion/CLIP-ViT-B-32-DataComp.XL-s13B-b90K",
"laion/CLIP-ViT-B-16-DataComp.XL-s13B-b90K",
"laion/CLIP-ViT-L-14-DataComp.XL-s13B-b90K",
"laion/CLIP-ViT-L-14-DataComp.XL-s13B-b90K",
"laion/CLIP-ViT-B-32-DataComp.XL-s13B-b90K",
"laion/CLIP-ViT-B-16-DataComp.XL-s13B-b90K",
"laion/CLIP-ViT-bigG-14-laion2B-39B-b160k",
"laion/CLIP-ViT-g-14-laion2B-s34B-b88K",
"laion/CLIP-ViT-H-14-laion2B-s32B-b79K",
"laion/CLIP-ViT-L-14-laion2B-s32B-b82K",
"laion/CLIP-ViT-B-32-laion2B-s34B-b79K",
"EVA02-CLIP-B-16",
"EVA02-CLIP-L-14",
"EVA02-CLIP-bigE-14",
"EVA02-CLIP-bigE-14-plus",
"TIGER-Lab/VLM2Vec-LoRA",
"TIGER-Lab/VLM2Vec-Full",
# run if enough compute:
# "Salesforce/blip-itm-base-coco",
# "Salesforce/blip-itm-large-coco",
# "Salesforce/blip-itm-base-flickr",
# "Salesforce/blip-itm-large-flickr",
# "kakaobrain/align-base",
# "jinaai/jina-clip-v1",
# "nomic-ai/nomic-embed-vision-v1.5",
# "Salesforce/blip2-opt-2.7b",
# "Salesforce/blip2-opt-6.7b-coco",
# "embed-english-v3.0-v", # not feasible to run due to the 40 images/min constraint
]:
# 16 by default already

for k_shot in [8, 32, 64, 128, 256]:
model = mteb.get_model(model_name)
tasks = mteb.get_tasks(
task_types=[
"ImageClassification",
]
)
for task in tasks:
task.samples_per_label = k_shot
evaluation = mteb.MTEB(tasks=tasks)
results = evaluation.run(
model, output_folder=f"results-mieb-final/linear_probe_{k_shot}"
)
30 changes: 30 additions & 0 deletions scripts/run_mieb_rerun_siglip.py
Original file line number Diff line number Diff line change
@@ -0,0 +1,30 @@
from __future__ import annotations

import mteb

for model_name in [
"google/siglip-so400m-patch14-384",
"google/siglip-base-patch16-256-multilingual",
"google/siglip-base-patch16-256",
"google/siglip-base-patch16-512",
"google/siglip-base-patch16-384",
"google/siglip-base-patch16-224",
"google/siglip-large-patch16-256",
"google/siglip-large-patch16-384",
]:
model = mteb.get_model(model_name)
tasks = mteb.get_tasks(
task_types=[
"Any2AnyRetrieval",
"Any2AnyMultiChoice",
"Any2TextMutipleChoice",
"ImageClustering",
"ImageClassification",
"ImageMultilabelClassification",
"ImageTextPairClassification",
# "VisualSTS", # visual sts does not need rerun as will be the same after fixed.
"ZeroShotClassification",
]
)
evaluation = mteb.MTEB(tasks=tasks)
results = evaluation.run(model, output_folder="results-mieb-final/siglip_rerun")
Loading