Delete MMEB-V3

MMEB-V3/mscoco_omni/.gitattributes

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-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.avro filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
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-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.lz4 filter=lfs diff=lfs merge=lfs -text
-*.mds filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
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-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
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-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
-# Audio files - uncompressed
-*.pcm filter=lfs diff=lfs merge=lfs -text
-*.sam filter=lfs diff=lfs merge=lfs -text
-*.raw filter=lfs diff=lfs merge=lfs -text
-# Audio files - compressed
-*.aac filter=lfs diff=lfs merge=lfs -text
-*.flac filter=lfs diff=lfs merge=lfs -text
-*.mp3 filter=lfs diff=lfs merge=lfs -text
-*.ogg filter=lfs diff=lfs merge=lfs -text
-*.wav filter=lfs diff=lfs merge=lfs -text
-# Image files - uncompressed
-*.bmp filter=lfs diff=lfs merge=lfs -text
-*.gif filter=lfs diff=lfs merge=lfs -text
-*.png filter=lfs diff=lfs merge=lfs -text
-*.tiff filter=lfs diff=lfs merge=lfs -text
-# Image files - compressed
-*.jpg filter=lfs diff=lfs merge=lfs -text
-*.jpeg filter=lfs diff=lfs merge=lfs -text
-*.webp filter=lfs diff=lfs merge=lfs -text
-# Video files - compressed
-*.mp4 filter=lfs diff=lfs merge=lfs -text
-*.webm filter=lfs diff=lfs merge=lfs -text

MMEB-V3/mscoco_omni/README.md

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----
-license: cc-by-4.0
----
-#
-**catalog.jsonl** contains the captions and filenames for each image id. There are around 170 text-image-video-audio (omni) tuples in it.
-# How to run
-1. Specify GOOGLE_API_KEY
-2. run the following to get setup ready
-   ```bash
-   bash setup.sh
-   ```
-3. then only need to run this file only in the future
-   ```bash
-   python geminiAPI.py
-   ```
-4. If runs successfully, a file called **mscoco_cmret.jsonl** will be generated, please provide this file to me.

MMEB-V3/mscoco_omni/audios.tar.gz

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-oid sha256:2c9e6f5a737cfb84d861a3b6fe409e2a46ec054bdf059b347eb33c10cfbf344e
-size 126042220

MMEB-V3/mscoco_omni/geminiAPI.py

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-from google import genai
-from google.genai import types
-from tqdm import tqdm
-from pycocotools.coco import COCO
-import pandas as pd
-import matplotlib.pyplot as plt
-import time
-import os
-import wave
-import subprocess
-from PIL import Image
-
-import time
-from google.genai.errors import ClientError
-
-def prRed(s): print("\033[1;31m {}\033[0m".format(s))
-def prGreen(s): print("\033[92m {}\033[00m".format(s))
-def prYellow(s): print("\033[93m {}\033[00m".format(s))
-def prBlue(s): print("\033[94m {}\033[00m".format(s))
-def prOrange(s): print("\033[38;5;214m {}\033[00m".format(s))
-def prPurple(s): print("\033[95m {}\033[00m".format(s))
-def prCyan(s): print("\033[96m {}\033[00m".format(s))
-def prLightGray(s): print("\033[97m {}\033[00m".format(s))
-def prBlack(s): print("\033[90m {}\033[00m".format(s))
-
-def get_id_with_filename(file_name):
-    img_id = os.path.basename(file_name).split('.')[0]
-    img_id = img_id.split('_')[-1]
-    return int(img_id)
-
-def get_filename_with_id(id, format='jpg'):
-    return f'COCO_val2014_{str(id).zfill(12)}.{format}'
-
-def plot_grouped_imgs(loi, ncols=5, figsize=(20, 20)):
-    nrows = (len(loi) + ncols - 1) // ncols
-    fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=figsize)
-    for i, img_path in enumerate(loi):
-        row = i // ncols
-        col = i % ncols
-        axes[row, col].imshow(Image.open(img_path))
-        axes[row, col].set_title(os.path.basename(img_path))
-        axes[row, col].axis('off')
-    plt.tight_layout()
-    plt.show()
-    return fig
-
-def generate_video_with_retry(client, image, text, max_retries=20):
-    for attempt in range(max_retries):
-        prYellow(f'Attempt {attempt} ...')
-        try:
-            return generate_video_from_image_and_text(client, image, text)
-        except ClientError as e:
-            if e.code != 429:
-                raise
-            if attempt < max_retries - 1:
-                wait_time = min(2 ** attempt, 64)  # Exponential backoff: 1s, 2s, 4s
-                prRed(f"Connection error: {e}. Retrying in {wait_time}s... (attempt {attempt + 1}/{max_retries})")
-                time.sleep(wait_time)
-            else:
-                prRed(f"Failed after {max_retries} attempts")
-                raise
-
-def generate_audio_with_retry(client, text, max_retries=20):
-    for attempt in range(max_retries):
-        prYellow(f'Attempt {attempt+1} ...')
-        try:
-            return generate_audio_from_text(client, text)
-        except ClientError as e:
-            if e.code != 429:
-                raise
-            if attempt < max_retries - 1:
-                wait_time = min(2 ** attempt, 64)
-                prRed(f"Connection error: {e}. Retrying in {wait_time}s... (attempt {attempt + 1}/{max_retries})")
-                time.sleep(wait_time)
-            else:
-                prRed(f"Failed after {max_retries} attempts")
-                raise
-
-
-
-def load_image(image_path):
-    with open(image_path, "rb") as f:
-        image_bytes = f.read()
-    return types.Image(mime_type="image/jpeg", image_bytes=image_bytes)
-
-def generate_prompt(client, prompt):
-    transcript = client.models.generate_content(
-                    model="gemini-2.5-flash",
-                    contents="""Generate a short transcript based on the following description: """ + prompt).text
-    return transcript
-
-def generate_image_from_text(client, text):
-    response = client.models.generate_content(
-        model="gemini-2.5-flash-image",
-        contents=[text],
-        config={"response_modalities":['IMAGE']}
-    )
-    return response.candidates[0].content.parts[0].as_image()
-
-def wave_file(filename, pcm, channels=1, rate=24000, sample_width=2):
-    """Save PCM audio data to a .wav file.
-    """
-    with wave.open(filename, "wb") as wf:
-        wf.setnchannels(channels)
-        wf.setsampwidth(sample_width)
-        wf.setframerate(rate)
-        wf.writeframes(pcm)
-
-def generate_audio_from_text(client, text):
-    response = client.models.generate_content(
-    model="gemini-2.5-flash-preview-tts",
-    contents=text,
-    config=types.GenerateContentConfig(
-        response_modalities=["AUDIO"],
-        speech_config=types.SpeechConfig(
-            voice_config=types.VoiceConfig(
-                prebuilt_voice_config=types.PrebuiltVoiceConfig(
-                voice_name='Kore',
-                )
-            )
-        ),
-    )
-    )
-
-    data = response.candidates[0].content.parts[0].inline_data.data
-    return data
-
-def generate_video_from_image_and_text(client, image, text):
-    operation = client.models.generate_videos(
-        model="veo-3.1-fast-generate-preview",
-        prompt=text,
-        image=image,
-        config=types.GenerateVideosConfig(
-            aspect_ratio="16:9",
-            resolution="720p",
-            duration_seconds="8",
-        )
-    )
-
-    # Poll the operation status until the video is ready.
-    while not operation.done:
-        prCyan("Waiting for video generation to complete...")
-        time.sleep(10)
-        operation = client.operations.get(operation)
-
-    if operation.result and operation.result.generated_videos:
-        generated_video = operation.result.generated_videos[0]
-        return generated_video.video
-    elif operation.response and operation.response.generated_videos:
-        generated_video = operation.response.generated_videos[0]
-        return generated_video.video
-    else:
-        # Handle cases where the model might fail (e.g. safety filters)
-        prRed(f"Generation failed: {operation}")
-        if not operation.response:
-            raise RuntimeError(f"Unknown reasons for {operation} to be None")
-        raise RuntimeError(f"Generation failed or was filtered: {operation.response.rai_media_filtered_reasons}")
-
-# def main():
-#     with open('allowed_ids.json', 'r') as f:
-#         allowed_ids = json.load(f)
-
-#     num = 100
-#     data = load_dataset('json', data_files='MSCOCO_t2i_test.jsonl', split='train')
-#     data = data.shuffle(seed=42).select(range(num))
-
-#     client = genai.Client()
-#     ttv_prompt_instruct = "Generate a video based on the following image and description: "
-#     tts_prompt_instruct = "Read aloud the following sentence in a natural and expressive way, in a warm and friendly tone: "
-
-#     save_dir = './output/'
-#     os.makedirs(save_dir, exist_ok=True)
-
-#     infos = []
-#     for item in tqdm(data):
-#         image_path = item['tgt_img_path'][0]
-#         save_name = os.path.basename(image_path).split('.')[0]
-#         save_name_mp4 = f'{save_name}.mp4'
-#         save_name_wav = f'{save_name}.wav'
-#         caption = item['qry_text']
-#         print(f"Processing {save_name} with caption: {caption}")
-
-#         if save_name not in allowed_ids:
-#             print(f"🛑 {save_name} not in allowed IDs, skipping this item.")
-#             continue
-
-#         existing_objects = list(set([obj.split('.')[0] for obj in os.listdir(save_dir)]))
-#         if save_name in existing_objects:
-#             continue
-
-#         image = load_image(os.path.join(data_dir, image_path))
-#         try:
-#             video_prompt = ttv_prompt_instruct + caption
-#             # transcript = generate_prompt(client, video_prompt)
-#             # print(f"Generated transcript: {transcript}")
-#             video_data = generate_video_from_image_and_text(client, image, video_prompt)
-#             video_filename = os.path.join(save_dir, save_name_mp4)
-#             client.files.download(file=video_data)
-#             video_data.save(video_filename)
-#             print(f"Generated video saved to {video_filename}")
-
-#             audio_prompt = tts_prompt_instruct + caption
-#             audio_data = generate_audio_from_text(client, audio_prompt)
-#             audio_filename = os.path.join(save_dir, save_name_wav)
-#             wave_file(audio_filename, audio_data)
-#             print(f"Generated audio saved to {audio_filename}")
-#         except (TypeError, RuntimeError) as e:
-#             print(f"🛑 Error processing {save_name}: {e}, skipping this item.")
-#             continue
-
-#         # always update negative videos, even if the video already exists
-#         raw_neg_objs = [os.path.basename(img_path).split('.')[0] for img_path in item['tgt_img_path'][1:]]
-#         neg_obj_list = list(set(raw_neg_objs) & set(existing_objects))
-#         infos.append({
-#             "id": save_name,
-#             "qry_text": caption,
-#             "qry_image_path": image_path, 
-#             "negatives": neg_obj_list,
-#         })
-#     results = pd.DataFrame(infos)
-#     results.to_json('MSCOCO_ti2v.jsonl', orient='records', lines=True)
-#     print("✅ All done! Results saved to MSCOCO_ti2v.jsonl")
-
-def main():
-    coco_caps = COCO("annotations/captions_val2014.json")
-
-    client = genai.Client()
-    ttv_prompt_instruct = "Generate a video based on the following image and description: "
-    tts_prompt_instruct = "Read aloud the following sentence in a natural and expressive way, in a warm and friendly tone: "
-
-    data_dir = 'val2014'
-    video_save_dir = 'videos'
-    audio_save_dir = 'audios'
-    os.makedirs(video_save_dir, exist_ok=True)
-    os.makedirs(audio_save_dir, exist_ok=True)
-
-    infos = pd.read_json('mscoco_cmret_all.jsonl', lines=True)
-    infos = infos[['image_id', 'file_name', 'hard_negatives']].set_index('image_id', drop=False)
-    records = []
-    with open('invalid.log') as f:
-        invalids = f.read().splitlines()
-    prBlue(f"Invalid ids: {invalids}")
-
-    for info in tqdm(infos.itertuples(), total=len(infos)):
-        image_id = info.image_id
-        file_name = info.file_name
-        negatives = info.hard_negatives
-
-        captions = [ann['caption'] for ann in coco_caps.loadAnns(coco_caps.getAnnIds(imgIds=image_id))]
-        qry_text = captions[-1] # we use different caption as query text, to make it harder
-
-        prBlue(f"Processing {file_name}\nQuery text: {qry_text}")
-
-        image = load_image(os.path.join(data_dir, file_name))
-        generated_negatives = []
-        for cand_id in tqdm([image_id]+negatives[:10], leave=True, desc="Waiting for API responses ... "):
-            if str(cand_id) in invalids:
-                prYellow(f"{cand_id} contains restricted content, skipping ... ")
-                continue
-            
-            captions = [ann['caption'] for ann in coco_caps.loadAnns(coco_caps.getAnnIds(imgIds=cand_id))]
-            caption = captions[0] # use first caption to generate video and audio
-            image = load_image(os.path.join(data_dir, get_filename_with_id(cand_id)))
-            cand_video_name = get_filename_with_id(cand_id, format='mp4')
-            cand_audio_name = get_filename_with_id(cand_id, format='wav')
-            
-            try:
-                video_save_path = os.path.join(video_save_dir, cand_video_name)
-                if os.path.exists(video_save_path):
-                    prGreen(f"Video {cand_id} already exists, skipping generation.")
-                else:
-                    prYellow(f"Generating video ...Caption: {caption}")
-                    video_prompt = ttv_prompt_instruct + caption
-                    video_data = generate_video_with_retry(client, image, video_prompt, max_retries=20) # change max_retries if needed
-                    client.files.download(file=video_data)
-                    video_data.save(video_save_path)
-                    prGreen(f"Generated video saved to {video_save_path}")
-
-                audio_save_path = os.path.join(audio_save_dir, cand_audio_name)
-                if os.path.exists(audio_save_path):
-                    prGreen(f"Audio {cand_id} already exists, skipping generation.")
-                else:
-                    prYellow(f"Generating audio ...Caption: {caption}")
-                    audio_prompt = tts_prompt_instruct + caption
-                    audio_data = generate_audio_with_retry(client, audio_prompt, max_retries=20) # change max_retries if needed
-                    wave_file(audio_save_path, audio_data)
-                    prGreen(f"Generated audio saved to {audio_save_path}")
-                generated_negatives.append(cand_id)
-
-
-            except (TypeError, RuntimeError) as e:
-                invalids.append(cand_id)
-                with open('invalid.log', 'a') as f:
-                    f.write(f'{cand_id}\n')
-                prRed(f"🛑 Error processing {cand_video_name}: {e}, skipping this item.")
-                continue
-
-        records.append({
-            "image_id": image_id,
-            "qry_text": qry_text,
-            "hard_negatives": [idx for idx in generated_negatives if idx != image_id]
-        })
-    records = pd.DataFrame(records)
-    records.to_json('mscoco_cmret.jsonl', orient='records', lines=True)
-
-if __name__ == "__main__":
-    if not os.path.exists("annotations/instances_val2014.json"):
-        subprocess.run(["wget", "http://images.cocodataset.org/annotations/annotations_trainval2014.zip"])
-        subprocess.run(["unzip", "annotations_trainval2014.zip", "-d", "annotations"])
-        subprocess.run(["rm", "annotations_trainval2014.zip"])
-        subprocess.run(["wget", "http://images.cocodataset.org/zips/val2014.zip"])
-        subprocess.run(["unzip", "val2014.zip", "-d", "."])
-        subprocess.run(["rm", "val2014.zip"])
-    main()
-    # client = genai.Client()
-    # video = generate_video_with_retry(client, load_image('val2014/COCO_val2014_000000176744.jpg'), "Generate a video based on the following image and description: Croweded area on the beach with many kites being flown in the air.")
-    # client.files.download(file=video)
-    # video.save('mscoco_omini/COCO_val2014_000000176744.mp4')
-
-    # audio = generate_audio_with_retry(client, "Read aloud the following sentence in a natural and expressive way, in a warm and friendly tone: Croweded area on the beach with many kites being flown in the air.")
-    # wave_file('mscoco_omini/COCO_val2014_000000176744.wav', audio)

MMEB-V3/mscoco_omni/invalid.log

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MMEB-V3/mscoco_omni/mscoco_cmret_all.jsonl

@@ -1,113 +0,0 @@
-{"image_id":2295,"file_name":"COCO_val2014_000000002295.jpg","objects":["bowl","dining table","cat"],"hard_negatives":[347113,99269,554291,183967,157876,40414,397133,60623,374628,19358,452684,350341,309852,26942,383406,311553,280819,269089]}
-{"image_id":3067,"file_name":"COCO_val2014_000000003067.jpg","objects":["sandwich","apple","cat"],"hard_negatives":[715,339442,229191,425526,411832,563723,254838,360209,21161,566644,374628]}
-{"image_id":11291,"file_name":"COCO_val2014_000000011291.jpg","objects":["tv","person","wine glass","bottle"],"hard_negatives":[300403,123677,242051,180751,209530,33052,502671,273329,225913,263323,137260,480000,368402,203849,195645,31442,363840,367398]}
-{"image_id":17182,"file_name":"COCO_val2014_000000017182.jpg","objects":["chair","book","apple"],"hard_negatives":[451468,177496,248919,156278,484351,303626,208995,374628,386912,165547,472795,26942,280819,143975,362373,65267,436470,400538,38034]}
-{"image_id":18046,"file_name":"COCO_val2014_000000018046.jpg","objects":["bus","horse"],"hard_negatives":[367680,23201,301937,26201,237326,190494,427655,224104,454798,432317,436127,95702,228477,122871,313718,395634,495117,548713,138246,37675]}
-{"image_id":19589,"file_name":"COCO_val2014_000000019589.jpg","objects":["book","bottle","vase","bowl","chair","couch"],"hard_negatives":[65267,463290,314757,366096,143247,445009,173434,491856,528062,303006,497444,292169,370899,267135,33052,471816,498489,185614]}
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MMEB-V3/mscoco_omni/requirements.txt

@@ -1,6 +0,0 @@
-google
-wave
-tadm
-pandas
-matplotlib
-pycocotools

MMEB-V3/mscoco_omni/setup.sh

@@ -1,14 +0,0 @@
-# download coco source images
-wget "http://images.cocodataset.org/annotations/annotations_trainval2014.zip"
-unzip "annotations_trainval2014.zip" -d "annotations"
-rm "annotations_trainval2014.zip"
-wget "http://images.cocodataset.org/zips/val2014.zip"
-unzip "val2014.zip", -d .
-rm "val2014.zip"
-
-pip install -r requirements.txt
-tar -xzf videos.tar.gz
-tar -xzf audios.tar.gz
-
-echo "Specify GOOGLE_API_KEY first ..."
-export GOOGLE_API_KEY=''

MMEB-V3/mscoco_omni/videos.tar.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:2f448f4bdc2d46aaa1a12d0825074a346b68f6c74a805febf515f9ac912658ef
-size 5374313848