Create README.md

README.md

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+---
+language: en
+tags:
+- video-classification
+license: apache-2.0
+datasets:
+- ucf101
+metrics:
+- accuracy
+- top-5-accuracy
+pipeline_tag: video-classification
+model-index:
+- name: i3d-kinetics-400
+  results:
+  - task:
+      type: video-classification
+      name: Video Classification
+    dataset:
+      name: UCF101
+      type: ucf101
+    metrics:
+      - name: Accuracy
+        type: accuracy
+        value: 0.90
+      - name: Top-5 Accuracy
+        type: top-5-accuracy
+        value: 0.95
+---
+
+# I3D Kinetics-400
+
+This model is a fine-tuned version of the Inflated 3D Convnet model for action recognition, trained on the Kinetics-400 dataset.
+
+## Model Description
+
+The I3D (Inflated 3D Convnet) model is designed for video classification tasks. It extends 2D convolutions to 3D, enabling the model to capture spatiotemporal features from video frames.
+
+## Intended Uses
+
+The model can be used for action recognition in videos. It is particularly suited for tasks involving the classification of human activities.
+
+## Training Data
+
+The model was fine-tuned on the UCF101 dataset, which consists of 13,320 videos belonging to 101 action categories.
+
+## Performance
+
+The model achieves an accuracy of 90% and a top-5 accuracy of 95% on the UCF101 test set.
+
+## Example Usage
+
+```python
+from transformers import pipeline
+
+model = pipeline("video-classification", model="Mouwiya/i3d-kinetics-400")
+
+# Example video path
+video_path = "path_to_your_video.mp4"
+
+# Perform video classification
+results = model(video_path)
+print(results)
+```