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phronetic-ai
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owlet-har-1

Video Classification
Transformers
Safetensors
qwen2_5_vl
image-text-to-text
llama-factory
activity-recognition
human-action-recognition
qwen2.5-vl-3B
vision-language-model
fine-tuned
hmdb51
text-generation-inference
Model card Files Community

Owlet-HAR-1: Human Activity Recognition Vision Language Model

Owlet-HAR-1 is a fine-tuned vision-language model specialized for human activity recognition in videos. Built on Qwen2.5-3B-VL, this model achieves 68.19% accuracy on the HMDB51 dataset, representing a significant improvement over the base model's 38.33% accuracy.

Model Details

Model Description

Owlet-HAR-1 is a specialized vision-language model fine-tuned for human activity recognition tasks. The model processes video input and classifies human activities across 51 different action categories, ranging from facial expressions to complex body movements and object interactions.

  • Developed by: Phronetic AI
  • Model type: Vision-Language Model (Video Classification)
  • Language(s): English (text output), Visual (video input)
  • License: Apache 2.0
  • Finetuned from model: Qwen/Qwen2.5-3B-Instruct
  • Specialized for: Human Activity Recognition in Videos

Model Sources

Uses

Direct Use

The model is designed for direct video activity recognition tasks. It takes video input and outputs a single word describing the primary human activity being performed. The model could be used for:

  • Healthcare monitoring: Identifying daily activities and movements
  • Human-computer interaction: Understanding user actions in video interfaces
  • Security and surveillance: Automated activity detection
  • Content analysis: Categorizing video content by human activities

Downstream Use

The model can be integrated into larger systems for:

  • Video content management systems
  • Automated video tagging and indexing
  • Real-time activity monitoring applications
  • Educational platforms for movement analysis
  • Assistive technologies for elderly or disabled individuals

Out-of-Scope Use

  • Privacy-sensitive applications: The model should not be used for unauthorized surveillance
  • High-stakes decision making: Not suitable for critical applications without human oversight
  • Real-time safety systems: May not be reliable enough for safety-critical applications
  • Non-human activity recognition: Trained specifically on human activities
  • Complex scene understanding: Focuses on single-person activities, may struggle with multi-person scenes

Performance

Key Metrics on HMDB51

  • Accuracy: 68.19%
  • Precision: 70.20%
  • Recall: 67.93%

Best Performing Activities

  • Cartwheeling, drawing_sword, falling, grooming, punching: 100% precision and recall
  • Climbing_stairs: 95.24% recall, 90.91% precision
  • Drinking: 93.10% recall, 90.00% precision

Challenging Activities

  • Biking: 0% precision/recall (specific limitation)
  • Catching and shooting: Lower performance across metrics

How to Get Started with the Model 

from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info
import torch

# Load the model and processor
model = Qwen2VLForConditionalGeneration.from_pretrained(
    "phronetic-ai/owlet-har-1",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)
processor = AutoProcessor.from_pretrained("phronetic-ai/owlet-har-1", trust_remote_code=True)

# Video inference - Multiple input methods supported:

# Method 1: Using video frames as image list
messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "video",
                "video": [
                    "file:///path/to/frame1.jpg",
                    "file:///path/to/frame2.jpg",
                    "file:///path/to/frame3.jpg",
                    "file:///path/to/frame4.jpg",
                ],
            },
            {"type": "text", "text": "What's the activity the person is doing in this video? Answer in one word only."},
        ],
    }
]

# Method 2: Using local video file
messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "video",
                "video": "file:///path/to/your_video.mp4",
                "max_pixels": 360 * 420,
                "fps": 1.0,
            },
            {"type": "text", "text": "What's the activity the person is doing in this video? Answer in one word only."},
        ],
    }
]

# Method 3: Using video URL
messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "video",
                "video": "https://your-video-url.com/video.mp4",
            },
            {"type": "text", "text": "What's the activity the person is doing in this video? Answer in one word only."},
        ],
    }
]

# Preparation for inference
text = processor.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs, video_kwargs = process_vision_info(messages, return_video_kwargs=True)
inputs = processor(
    text=[text],
    images=image_inputs,
    videos=video_inputs,
    padding=True,
    return_tensors="pt",
    **video_kwargs,
)
inputs = inputs.to("cuda")

# Inference
generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [
    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)

print(f"Detected activity: {output_text[0]}")

Training Details

Training Data

The model was fine-tuned on the HMDB51 dataset, which contains:

  • Total clips: 6,849 video clips
  • Categories: 51 distinct human action categories
  • Category groups:
    • General Facial Actions (smile, laugh, chew, talk)
    • Facial Actions with Object Manipulation (smoke, eat, drink)
    • General Body Movements (cartwheel, handstand, jump, run, walk)
    • Body Movements with Object Interaction (brush hair, catch, golf, shoot ball)
    • Body Movements for Human Interaction (fencing, hug, kiss, punch)

Training Procedure

Training Hyperparameters

  • Learning rate: 5e-05 with cosine annealing
  • Training epochs: 3
  • Batch size: 16 (2 per device × 8 gradient accumulation steps)
  • Fine-tuning method: LoRA (Low-Rank Adaptation) with rank 8
  • Training regime: BF16 mixed precision
  • Optimization: AdamW optimizer
  • Framework: LLaMA-Factory

Compute Infrastructure

  • Hardware: AWS g5.2xlarge instances with NVIDIA A10G GPUs
  • Training time: Approximately 3 epochs on full HMDB51 dataset
  • Memory optimization: LoRA fine-tuning with BF16 precision for memory efficiency

Evaluation

Testing Data

Evaluated on HMDB51 test split using the same 51 activity.

Metrics

  • Accuracy: Overall classification accuracy across all categories
  • Precision: Per-category and macro-averaged precision
  • Recall: Per-category and macro-averaged recall

Results Summary

The model demonstrates strong performance on structured activities (gymnastics, specific movements) but struggles with activities involving rapid motion or complex object interactions. The 68.19% accuracy represents a 29.86 percentage point improvement over the base Qwen2.5-3B-VL model.

Bias, Risks, and Limitations

Known Limitations

  • Dataset bias: Trained on HMDB51, which may not represent all human activities or demographics
  • Single-person focus: Optimized for single-person activities, may struggle with multi-person scenes
  • Video quality dependency: Performance may degrade with poor lighting, low resolution, or occluded subjects
  • Cultural bias: Training data may not represent activities from all cultures equally
  • Temporal resolution: May miss very brief or subtle activities

Risk Considerations

  • Privacy concerns: Video analysis capabilities could be misused for surveillance
  • Misclassification impact: Incorrect classifications could lead to inappropriate automated responses

Technical Specifications

Model Architecture

  • Base Architecture: Qwen2.5-3B Vision-Language Model
  • Parameters: ~3 billion parameters
  • Fine-tuning Method: LoRA (Low-Rank Adaptation)
  • Input: Video sequences
  • Output: Text classification (single word activity label)
  • Context Length: Supports video sequences with multiple frames

Model Objective

The model is trained to classify human activities in videos using a text generation objective, where the model generates a single word representing the detected activity.

Citation

If you use this model in your research, please cite:

BibTeX:

@misc{owlet-har-1,
  title={Owlet-HAR-1: Human Activity Recognition Vision Language Model},
  author={Phronetic AI},
  year={2025},
  url={https://huggingface.co/phronetic-ai/owlet-har-1}
}

APA: Phronetic AI. (2024). Owlet-HAR-1: Human Activity Recognition Vision Language Model. Hugging Face. https://huggingface.co/phronetic-ai/owlet-har-1

Model Card Authors

Phronetic AI Research Team

Model Card Contact

For questions about this model, please contact: divyansh.makkar@phronetic.ai

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