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README.md

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-license: cc-by-nc-4.0
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+
+
+---
+license: "cc-by-nc-4.0"
+tags:
+- vision
+- video-classification
+---
+
+# FAL - Framework For Automated Labeling Of Videos (FALVideoClassifier)
+
+FAL (Framework for Automated Labeling Of Videos) is a custom video classification model developed by **SVECTOR** and fine-tuned on the **FAL-500** dataset. This model is designed for efficient video understanding and classification, leveraging state-of-the-art video processing techniques.
+
+## Model Overview
+
+This model, referred to as `FALVideoClassifier`, is built using a **TimeSformer-based architecture**, fine-tuned on **FAL-500**, and optimized for automated video labeling tasks. It is capable of classifying a video into one of the 400 possible labels from the FAL-500 dataset.
+
+This model was developed by **SVECTOR** as part of our initiative to advance automated video understanding and classification technologies. 
+
+## Intended Uses & Limitations
+
+This model is designed for video classification tasks, and you can use it to classify videos into one of the 400 classes from the FAL-500 dataset. Please note that the model was trained on **FAL-500** and may not perform as well on datasets that significantly differ from this.
+
+### Intended Use:
+- Automated video labeling
+- Video content classification
+- Research in video understanding and machine learning
+
+### Limitations:
+- Only trained on FAL-500
+- May not generalize well to out-of-domain videos without further fine-tuning
+- Requires videos to be pre-processed (such as resizing frames, normalization, etc.)
+
+## How to Use
+
+To use this model for video classification, follow these steps:
+
+### Installation:
+
+Ensure you have the necessary dependencies installed:
+
+```bash
+pip install torch torchvision transformers
+```
+
+### Code Example:
+
+Here is an example Python code snippet for using the FAL model to classify a video:
+
+```python
+from transformers import AutoImageProcessor, FALVideoClassifierForVideoClassification
+import numpy as np
+import torch
+
+# Simulating a sample video (8 frames of size 224x224 with 3 color channels)
+video = list(np.random.randn(8, 3, 224, 224))  # 8 frames, each of size 224x224 with RGB channels
+
+# Load the image processor and model
+processor = AutoImageProcessor.from_pretrained("SVECTOR-CORPORATION/FAL")
+model = FALVideoClassifierForVideoClassification.from_pretrained("SVECTOR-CORPORATION/FAL")
+
+# Pre-process the video input
+inputs = processor(video, return_tensors="pt")
+
+# Run inference with no gradient calculation (evaluation mode)
+with torch.no_grad():
+    outputs = model(**inputs)
+    logits = outputs.logits
+
+# Find the predicted class (highest logit)
+predicted_class_idx = logits.argmax(-1).item()
+
+# Output the predicted label
+print("Predicted class:", model.config.id2label[predicted_class_idx])
+```
+
+### Model Details:
+
+- **Model Name**: `FALVideoClassifier`
+- **Dataset Used**: FAL-S500
+- **Input Size**: 8 frames of size 224x224 with 3 color channels (RGB)
+
+### Configuration:
+
+The `FALVideoClassifier` uses the following hyperparameters:
+
+- `num_frames`: Number of frames in the video (e.g., 8)
+- `num_labels`: The number of possible video classes (500 for FAL-500)
+- `hidden_size`: Hidden size for transformer layers (768)
+- `attention_probs_dropout_prob`: Dropout probability for attention layers (0.0)
+- `hidden_dropout_prob`: Dropout probability for the hidden layers (0.0)
+- `drop_path_rate`: Dropout rate for stochastic depth (0.0)
+
+### Preprocessing:
+
+Before feeding videos into the model, ensure the frames are properly pre-processed:
+
+- Resize frames to `224x224`
+- Normalize pixel values (use the processor from the model, as shown in the code)
+
+## License
+
+This model is licensed under the **CC-BY-NC-4.0** license, which means it can be used for non-commercial purposes with proper attribution.
+
+## Citation
+
+If you use this model in your research or projects, please cite the following:
+
+```bibtex
+@inproceedings{bertasius2021space,
+  title={Is Space-Time Attention All You Need for Video Understanding?},
+  author={Bertasius, Gedas and Wang, Heng and Torresani, Lorenzo},
+  booktitle={International Conference on Machine Learning},
+  pages={813--824},
+  year={2021},
+  organization={PMLR}
+}
+```
+
+## Contact
+
+For any inquiries regarding this model or its implementation, you can contact the SVECTOR team at [email protected].
+
+---
+