Update README.md

README.md

@@ -2,199 +2,286 @@
 library_name: transformers
 tags:
 - llama-factory
+- video-classification
+- activity-recognition
+- human-action-recognition
+- qwen2.5-vl-3B
+- vision-language-model
+- fine-tuned
+- hmdb51
+license: apache-2.0
+base_model: Qwen/Qwen2.5-3B-Instruct
+datasets:
+- hmdb51
+metrics:
+- accuracy
+- precision
+- recall
+pipeline_tag: video-classification
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# 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
 
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+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:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **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 [optional]
+### Model Sources
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** https://huggingface.co/phronetic-ai/owlet-har-1
+- **Base Model:** https://huggingface.co/Qwen/Qwen2.5-3B-Instruct
+- **Research Blog:** [Enhancing Video Activity Recognition with Human Pose Data: A Vision Language Model Study](To be added)
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+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:
 
-[More Information Needed]
+- **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 [optional]
+### Downstream Use
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+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
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+- **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
 
-## Bias, Risks, and Limitations
+## Performance
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+### Key Metrics on HMDB51
 
-[More Information Needed]
+- **Accuracy:** 68.19%
+- **Precision:** 70.20%
+- **Recall:** 67.93%
 
-### Recommendations
+### 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
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+### Challenging Activities
+- Biking: 0% precision/recall (specific limitation)
+- Catching and shooting: Lower performance across metrics
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
-
-[More Information Needed]
+```python
+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
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
+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
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
 #### Training Hyperparameters
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+- **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
 
-#### Speeds, Sizes, Times [optional]
+#### Compute Infrastructure
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+- **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
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
+### Testing Data
 
-#### Factors
+Evaluated on HMDB51 test split using the same 51 activity.
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+### Metrics
 
-[More Information Needed]
+- **Accuracy:** Overall classification accuracy across all categories
+- **Precision:** Per-category and macro-averaged precision
+- **Recall:** Per-category and macro-averaged recall
 
-#### Metrics
+### Results Summary
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+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.
 
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+## Bias, Risks, and Limitations
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+### Known Limitations
 
-## Technical Specifications [optional]
+- **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
 
-### Model Architecture and Objective
+### Risk Considerations
 
-[More Information Needed]
+- **Privacy concerns:** Video analysis capabilities could be misused for surveillance
+- **Misclassification impact:** Incorrect classifications could lead to inappropriate automated responses
 
-### Compute Infrastructure
 
-[More Information Needed]
+## Technical Specifications
 
-#### Hardware
+### Model Architecture
 
-[More Information Needed]
+- **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
 
-#### Software
+### Model Objective
 
-[More Information Needed]
+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 [optional]
+## Citation
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+If you use this model in your research, please cite:
 
 **BibTeX:**
-
-[More Information Needed]
+```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
 
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
+## Model Card Authors
 
-[More Information Needed]
+Phronetic AI Research Team
 
 ## Model Card Contact
 
-[More Information Needed]
+For questions about this model, please contact: divyansh.makkar@phronetic.ai