caskcsg/Libra-Emo-1B

🎭 Libra-Emo Model

A Multimodal Large Language Model for Fine-Grained Negative Emotion Detection

This is the official model release of Libra-Emo, a multimodal large language model for fine-grained negative emotion detection. The model is built upon InternVL 2.5 and fine-tuned on our Libra-Emo Dataset.

📝 Model Description

Libra-Emo Model is designed to understand and analyze emotions in video content. It can:

• Recognize 13 fine-grained emotion categories
• Provide detailed explanations for emotion classifications
• Process both visual and textual (subtitle) information
• Handle real-world video scenarios with complex emotional expressions

🚀 Usage

Environment Setup

Our model is tested with CUDA 12.1. To set up the environment:

# Create and activate conda environment
conda create -n libra-emo python=3.10
conda activate libra-emo

# Clone and install InternVL dependencies
git clone https://github.com/OpenGVLab/InternVL.git
cd InternVL
pip install -r requirements/internvl_chat.txt

Usage Example

Here's a complete example of how to use Libra-Emo Model for video emotion analysis:

import numpy as np
import torch
import torchvision.transforms as T
from decord import VideoReader, cpu
from PIL import Image
from torchvision.transforms.functional import InterpolationMode
from transformers import AutoModel, AutoTokenizer

def build_transform(input_size):
    MEAN = (0.485, 0.456, 0.406)
    STD = (0.229, 0.224, 0.225)
    transform = T.Compose(
        [
            T.Lambda(lambda img: img.convert("RGB") if img.mode != "RGB" else img),
            T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
            T.ToTensor(),
            T.Normalize(mean=MEAN, std=STD),
        ]
    )
    return transform

def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
    best_ratio_diff = float("inf")
    best_ratio = (1, 1)
    area = width * height
    for ratio in target_ratios:
        target_aspect_ratio = ratio[0] / ratio[1]
        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
        if ratio_diff < best_ratio_diff:
            best_ratio_diff = ratio_diff
            best_ratio = ratio
        elif ratio_diff == best_ratio_diff:
            if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
                best_ratio = ratio
    return best_ratio

def dynamic_preprocess(
    image, min_num=1, max_num=12, image_size=448, use_thumbnail=False
):
    orig_width, orig_height = image.size
    aspect_ratio = orig_width / orig_height

    # calculate the existing image aspect ratio
    target_ratios = set(
        (i, j)
        for n in range(min_num, max_num + 1)
        for i in range(1, n + 1)
        for j in range(1, n + 1)
        if i * j <= max_num and i * j >= min_num
    )
    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])

    # find the closest aspect ratio to the target
    target_aspect_ratio = find_closest_aspect_ratio(
        aspect_ratio, target_ratios, orig_width, orig_height, image_size
    )

    # calculate the target width and height
    target_width = image_size * target_aspect_ratio[0]
    target_height = image_size * target_aspect_ratio[1]
    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]

    # resize the image
    resized_img = image.resize((target_width, target_height))
    processed_images = []
    for i in range(blocks):
        box = (
            (i % (target_width // image_size)) * image_size,
            (i // (target_width // image_size)) * image_size,
            ((i % (target_width // image_size)) + 1) * image_size,
            ((i // (target_width // image_size)) + 1) * image_size,
        )
        # split the image
        split_img = resized_img.crop(box)
        processed_images.append(split_img)
    assert len(processed_images) == blocks
    if use_thumbnail and len(processed_images) != 1:
        thumbnail_img = image.resize((image_size, image_size))
        processed_images.append(thumbnail_img)
    return processed_images

def load_image(image_file, input_size=448, max_num=12):
    image = Image.open(image_file).convert("RGB")
    transform = build_transform(input_size=input_size)
    images = dynamic_preprocess(
        image, image_size=input_size, use_thumbnail=True, max_num=max_num
    )
    pixel_values = [transform(image) for image in images]
    pixel_values = torch.stack(pixel_values)
    return pixel_values

def get_index(bound, fps, max_frame, first_idx=0, num_segments=32):
    if bound:
        start, end = bound[0], bound[1]
    else:
        start, end = -100000, 100000
    start_idx = max(first_idx, round(start * fps))
    end_idx = min(round(end * fps), max_frame)
    seg_size = float(end_idx - start_idx) / num_segments
    frame_indices = np.array(
        [
            int(start_idx + (seg_size / 2) + np.round(seg_size * idx))
            for idx in range(num_segments)
        ]
    )
    return frame_indices

def load_video(video_path, bound=None, input_size=448, max_num=1, num_segments=32):
    vr = VideoReader(video_path, ctx=cpu(0), num_threads=1)
    max_frame = len(vr) - 1
    fps = float(vr.get_avg_fps())

    pixel_values_list, num_patches_list = [], []
    transform = build_transform(input_size=input_size)
    frame_indices = get_index(
        bound, fps, max_frame, first_idx=0, num_segments=num_segments
    )
    for frame_index in frame_indices:
        img = Image.fromarray(vr[frame_index].asnumpy()).convert("RGB")
        img = dynamic_preprocess(
            img, image_size=input_size, use_thumbnail=True, max_num=max_num
        )
        pixel_values = [transform(tile) for tile in img]
        pixel_values = torch.stack(pixel_values)
        num_patches_list.append(pixel_values.shape[0])
        pixel_values_list.append(pixel_values)
    pixel_values = torch.cat(pixel_values_list)
    return pixel_values, num_patches_list
    

# Step 1: load the model
# If you have an 80G A100 GPU, you can put the entire model on a single GPU.
model_path = "caskcsg/Libra-Emo-1B"
model = AutoModel.from_pretrained(
    model_path,
    torch_dtype=torch.bfloat16,
    low_cpu_mem_usage=True,
    trust_remote_code=True,
    device_map="cuda:0"
)
model.eval()
tokenizer = AutoTokenizer.from_pretrained(
    model_path, trust_remote_code=True, use_fast=False
)

# Step 2: load the video
video_path = "your_video_path" # change to your video path
pixel_values, num_patches_list = load_video(
    video_path, num_segments=16, max_num=1
)
pixel_values = pixel_values.to(torch.bfloat16).to(model.device)
video_prefix = "".join(
    [f"Frame-{i+1}: <image>\n" for i in range(len(num_patches_list))]
)

# Step 3: set the question
subtitle = None # change to your subtitle (subtitle is optional, if you don't have subtitle, please set it to None)
if subtitle is None:
    question = (
        video_prefix
        + "The above is a video. Please accurately identify the emotional label expressed by the people in the video. Emotional labels include should be limited to: happy, excited, angry, disgusted, hateful, surprised, amazed, frustrated, sad, fearful, despairful, ironic, neutral. The output format should be:\n[label]\n[explanation]"
    )
else:
    question = (
        video_prefix
        + f"The above is a video. The video's subtitle is '{subtitle}', which maybe the words spoken by the person. Please accurately identify the emotional label expressed by the people in the video. Emotional labels include should be limited to: happy, excited, angry, disgusted, hateful, surprised, amazed, frustrated, sad, fearful, despairful, ironic, neutral. The output format should be:\n[label]\n[explanation]"
    )

# Step 4: generate the response
response, history = model.chat(
    tokenizer,
    pixel_values,
    question,
    dict(max_new_tokens=512, do_sample=False),
    num_patches_list=num_patches_list,
    history=None,
    return_history=True,
)
print(response)

The model will output the emotion label and explanation in the following format:

[label]
[explanation]

Note: If you aim to obtain emotion labels more quickly without requiring explanations, consider reducing the max_new_tokens value in the generation configuration.

📊 Performance Comparison

We evaluate our models on the Libra-Emo Bench, comparing with both closed-source and open-source models. The evaluation metrics include accuracy and F1 scores for all emotions (13 classes) and negative emotions (8 classes).

Performance Comparison of MLLMs on Libra-Emo Bench

Model	Accuracy	Macro-F1	Weighted-F1	Accuracy (Neg)	Macro-F1 (Neg)	Weighted-F1 (Neg)
Closed-Source Models
Gemini-2.0-Flash	65.67	63.98	64.51	65.00	62.97	63.86
Gemini-1.5-Flash	64.41	62.36	62.52	61.32	58.85	58.74
GPT-4o	62.99	63.56	63.32	67.89	67.54	67.89
Claude-3.5-Sonnet	52.13	48.38	49.38	49.47	49.32	50.50
Open-Source Models
LLaVA-Video-7B-Qwen2	33.39	30.14	31.25	22.11	25.55	26.65
MiniCPM-o 2.6 (8B)	42.83	40.23	40.26	40.53	37.29	38.00
Qwen2.5-VL-7B	47.56	44.18	43.68	41.32	39.07	38.50
NVILA-8B	41.89	35.92	36.01	42.89	32.83	33.88
Phi-3.5-vision-instruct	53.39	51.23	51.16	52.89	49.97	49.98
InternVL-2.5-1B	23.46	17.33	18.14	22.11	16.48	17.26
InternVL-2.5-2B	25.98	22.31	22.19	30.79	24.97	24.59
InternVL-2.5-4B	42.99	39.58	38.81	37.89	38.78	38.55
InternVL-2.5-8B	54.96	51.42	51.64	50.53	47.07	47.22
Fine-Tuned on Libra-Emo
Libra-Emo-1B	53.54 (↑30.08)	49.44 (↑32.11)	50.19 (↑32.05)	46.84 (↑24.73)	41.53 (↑25.05)	42.25 (↑24.99)
Libra-Emo-2B	56.38 (↑30.40)	53.60 (↑31.29)	53.90 (↑31.71)	50.26 (↑19.47)	48.79 (↑23.82)	48.91 (↑24.32)
Libra-Emo-4B	65.20 (↑22.21)	64.12 (↑24.54)	64.41 (↑25.60)	60.79 (↑22.90)	61.30 (↑22.52)	61.61 (↑23.06)
Libra-Emo-8B	71.18 (↑16.22)	70.51 (↑19.09)	70.71 (↑19.07)	70.53 (↑20.00)	69.94 (↑22.87)	70.14 (↑22.92)

Key Findings

1. Our Libra-Emo models significantly outperform their base InternVL models, with improvements up to 30% in accuracy and F1 scores.
2. The 8B version achieves the best performance, reaching 71.18% accuracy and 70.51% macro-F1 score on all emotions.
3. For negative emotions, our models show strong performance with up to 70.53% accuracy and 70.14% weighted-F1 score.
4. The performance scales well with model size, showing consistent improvements from 1B to 8B parameters.

Note: Our technical report with detailed methodology and analysis will be released soon.