Update README.md

README.md

@@ -1,199 +1,244 @@
 ---
 library_name: transformers
-tags: []
+tags:
+- video-classification
+- vjepa2
+- computer-vision
+- video-understanding
+- fine-tuned
+- pytorch
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# Model Card for VJEPA2 Fine-tuned Video Classification Model
 
+This model is a fine-tuned version of Facebook's VJEPA2 (Video Joint Embedding Predictive Architecture) for video classification tasks. The model has been fine-tuned using gradient accumulation and frozen backbone techniques for efficient training.
 
 ## 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.
-
-- **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]
+This is a fine-tuned VJEPA2 model specifically adapted for video classification tasks. The model leverages the pre-trained VJEPA2 backbone with a custom classification head, trained using efficient fine-tuning techniques including backbone freezing and gradient accumulation.
 
-### Model Sources [optional]
+- **Developed by:** Yiqiao Yin
+- **Funded by:** Yiqiao Yin
+- **Model type:** Video Classification
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0
+- **Finetuned from model:** qubvel-hf/vjepa2-vitl-fpc16-256-ssv2
 
-<!-- Provide the basic links for the model. -->
+### Model Sources
 
 - **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Paper:** [V-JEPA: Video Joint Embedding Predictive Architecture](https://arxiv.org/abs/2301.08243)
+- **Base Model:** [qubvel-hf/vjepa2-vitl-fpc16-256-ssv2](https://huggingface.co/qubvel-hf/vjepa2-vitl-fpc16-256-ssv2)
 
 ## 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. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
+This model can be directly used for video classification tasks. It processes video inputs and outputs class predictions based on the learned representations from the VJEPA2 backbone.
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+### Downstream Use
 
-[More Information Needed]
+The model can be further fine-tuned for specific video understanding tasks such as:
+- Action recognition
+- Video content classification
+- Temporal activity detection
+- Video scene understanding
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+This model is not intended for:
+- Real-time video processing applications requiring sub-second inference
+- High-resolution video analysis beyond the training resolution
+- Audio-based video classification (visual features only)
+- Video generation or synthesis tasks
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+The model inherits biases from the original VJEPA2 pre-training data and may exhibit performance variations across different video domains, lighting conditions, and demographic representations in video content.
 
 ### Recommendations
 
-<!-- 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.
+Users should evaluate the model's performance on their specific use case and consider additional fine-tuning if the target domain differs significantly from the training data. Monitor for potential biases in video content classification across different demographic groups.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
-
-[More Information Needed]
+Use the code below to get started with the model:
+
+```python
+import torch
+from transformers import VJEPA2VideoProcessor, VJEPA2ForVideoClassification
+
+# Load the model and processor
+model_name = "qubvel-hf/vjepa2-vitl-fpc16-256-ssv2"
+processor = VJEPA2VideoProcessor.from_pretrained(model_name)
+model = VJEPA2ForVideoClassification.from_pretrained(
+    model_name,
+    torch_dtype=torch.float32,
+    label2id=label2id,  # Your label mapping
+    id2label=id2label,  # Your ID to label mapping
+    ignore_mismatched_sizes=True,
+).to("cuda")
+
+# Process video and get predictions
+inputs = processor(video_data, return_tensors="pt").to(model.device)
+outputs = model(**inputs)
+predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+```
 
 ## 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 a custom video classification dataset. The specific dataset details depend on the user's implementation and target classification task.
 
 ### 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
 
-#### Preprocessing [optional]
+Videos are processed using the VJEPA2VideoProcessor, which handles:
+- Video frame extraction and normalization
+- Temporal sampling
+- Spatial resizing and augmentation
+- Tensor conversion for model input
 
-[More Information Needed]
+#### Training Hyperparameters
 
+- **Training regime:** FP32 precision
+- **Optimizer:** Adam
+- **Learning rate:** 1e-5
+- **Epochs:** 5
+- **Gradient accumulation steps:** 4
+- **Backbone freezing:** VJEPA2 backbone parameters frozen, only classification head trained
+- **Batch processing:** Gradient accumulation for effective larger batch size
 
-#### Training Hyperparameters
+#### Training Configuration
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+```python
+# Freeze backbone parameters
+for param in model.vjepa2.parameters():
+    param.requires_grad = False
 
-#### Speeds, Sizes, Times [optional]
+# Only train classification head
+trainable = [p for p in model.parameters() if p.requires_grad]
+optimizer = torch.optim.Adam(trainable, lr=1e-5)
+```
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+#### Speeds, Sizes, Times
 
-[More Information Needed]
+- **Training time:** Depends on dataset size and hardware
+- **GPU memory:** Optimized through gradient accumulation
+- **Effective batch size:** Original batch size × 4 (due to gradient accumulation)
 
 ## 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]
+The model is evaluated on held-out test sets from the training dataset, with validation performed after each epoch.
 
 #### Factors
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
+Evaluation considers:
+- Video content diversity
+- Temporal complexity
+- Visual quality variations
+- Classification difficulty across different classes
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+- **Primary metric:** Classification Accuracy
+- **Validation:** Per-epoch validation accuracy
+- **Final evaluation:** Test set accuracy
 
 ### Results
 
-[More Information Needed]
-
-#### Summary
-
+The model's performance is monitored through:
+- Training loss progression with gradient accumulation
+- Validation accuracy per epoch
+- Final test accuracy
+- TensorBoard logging for comprehensive monitoring
 
+## Model Examination
 
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+The model uses a frozen VJEPA2 backbone for feature extraction, with only the classification head being trained. This approach:
+- Preserves pre-trained video understanding capabilities
+- Reduces computational requirements
+- Prevents overfitting on smaller datasets
+- Enables efficient domain adaptation
 
 ## 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).
 
-- **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]
+- **Hardware Type:** NVIDIA GPU (CUDA-enabled)
+- **Hours used:** Dependent on dataset size and training configuration
+- **Training efficiency:** Optimized through gradient accumulation and backbone freezing
+- **Carbon Emitted:** Reduced due to efficient fine-tuning approach
 
-## Technical Specifications [optional]
+## Technical Specifications
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+- **Base Architecture:** VJEPA2 (Video Joint Embedding Predictive Architecture)
+- **Model Size:** ViT-Large with 16-frame processing capability
+- **Input Resolution:** 256x256 pixels
+- **Temporal Sampling:** 16 frames per video
+- **Classification Head:** Custom layer adapted to target classes
+- **Objective:** Cross-entropy loss for multi-class classification
 
 ### Compute Infrastructure
 
-[More Information Needed]
-
 #### Hardware
 
-[More Information Needed]
+- **GPU:** NVIDIA CUDA-compatible GPU
+- **Memory:** Sufficient VRAM for model and gradient accumulation
+- **Compute Capability:** CUDA support required
 
 #### Software
 
-[More Information Needed]
-
-## Citation [optional]
+- **Framework:** PyTorch
+- **Library:** Transformers (Hugging Face)
+- **Dependencies:** 
+  - torch
+  - transformers
+  - VJEPA2VideoProcessor
+  - VJEPA2ForVideoClassification
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+## Citation
 
 **BibTeX:**
 
-[More Information Needed]
+```bibtex
+@article{bardes2024vjepa,
+  title={V-JEPA: Video Joint Embedding Predictive Architecture},
+  author={Bardes, Adrien and Ponce, Jean and LeCun, Yann},
+  journal={arXiv preprint arXiv:2301.08243},
+  year={2024}
+}
+```
 
 **APA:**
 
-[More Information Needed]
-
-## Glossary [optional]
+Bardes, A., Ponce, J., & LeCun, Y. (2024). V-JEPA: Video Joint Embedding Predictive Architecture. arXiv preprint arXiv:2301.08243.
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+## Glossary
 
-[More Information Needed]
+- **VJEPA2:** Video Joint Embedding Predictive Architecture, second version
+- **Gradient Accumulation:** Technique to simulate larger batch sizes by accumulating gradients over multiple steps
+- **Backbone Freezing:** Training strategy where pre-trained layers are frozen and only task-specific layers are trained
+- **Video Classification:** Task of assigning categorical labels to video sequences
 
-## More Information [optional]
+## More Information
 
-[More Information Needed]
+For more details on the VJEPA2 architecture and training methodology, refer to the original paper and the base model documentation.
 
-## Model Card Authors [optional]
+## Model Card Authors
 
-[More Information Needed]
+Yiqiao Yin
 
 ## Model Card Contact
 
-[More Information Needed]
+For questions or issues regarding this model, please contact the model author or create an issue in the model repository.