Create README.md

README.md

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+---
+license: cc-by-4.0
+task_categories:
+- text-to-video
+language:
+- en
+tags:
+- text-to-video
+- Video Generative Model Training
+- Text-to-Video Diffusion Model Training
+- prompts
+pretty_name: OpenVid-1M
+size_categories:
+- 10K<n<100K
+dataset_info:
+  features:
+  - name: video
+    dtype: video
+  - name: caption
+    dtype: string
+  - name: aesthetic_score
+    dtype: float32
+  - name: motion_score
+    dtype: float32
+  - name: temporal_consistency_score
+    dtype: float32
+  - name: camera_motion
+    dtype: string
+  - name: frame
+    dtype: int32
+  - name: fps
+    dtype: float32
+  - name: seconds
+    dtype: float32
+  - name: part_id
+    dtype: int32
+---
+
+<p align="center">
+  <img src="https://huggingface.co/datasets/nkp37/OpenVid-1M/resolve/main/OpenVid-1M.png">
+</p>
+
+Combination of part_id's from [bigdata-pw/OpenVid-1M](https://huggingface.co/datasets/bigdata-pw/OpenVid-1M) and video data from [nkp37/OpenVid-1M](https://huggingface.co/datasets/nkp37/OpenVid-1M).
+
+This is a 10k video split of the original dataset for faster iteration during testing. The split was obtained by filtering on aesthetic and motion scores by iteratively increasing their values until there were at most 1000 videos. Only videos containing between 80 and 240 frames were considered.