nebius/SWE-agent-trajectories
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Ashish Soni
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Hello Hugging Face community!
I wanted to introduce myself and my company @Overlaiapp . We are a collective of filmmakers, photographers, and AI engineers working on high resolution (8K+) training data.
We plan to share a lot of our datasets with the community and are kicking things off with two curated datasets:
- Overlaiai/OregonCoastin4K
- Overlaiai/SubArcticPolarBear
Overlai.ai Dataset Features
🎥 Oversampled: Every clip is captured in stunning 8K resolution, delivering rich detail ideal for fine tuning scenic landscapes and ocean dynamics.
📸 Variance: Includes close-up details, slow-motion footage of crashing waves, sweeping landscapes, and wildlife shots.
📋 Detailed Metadata: Every clip is paired with structured metadata, including creative descriptions, precise camera movements, lens information, field of view calculations, and shot settings, ensuring AI models can fully understand and replicate real-world cinematography with accuracy.
⚙️ Consistency: Re-thinking training data at the point of capture by "overshooting" a subject, enabling models to learn more nuanced relationships and views across scenes.
🌅 Light: Shot during early morning and sunset light for optimal color contrast and dynamic range, maximizing visual quality for color and lighting-sensitive tasks.
🔍 Curation: Curated specifically for machine learning, providing clean, high-quality data for next generation model training.
I wanted to introduce myself and my company @Overlaiapp . We are a collective of filmmakers, photographers, and AI engineers working on high resolution (8K+) training data.
We plan to share a lot of our datasets with the community and are kicking things off with two curated datasets:
- Overlaiai/OregonCoastin4K
- Overlaiai/SubArcticPolarBear
Overlai.ai Dataset Features
🎥 Oversampled: Every clip is captured in stunning 8K resolution, delivering rich detail ideal for fine tuning scenic landscapes and ocean dynamics.
📸 Variance: Includes close-up details, slow-motion footage of crashing waves, sweeping landscapes, and wildlife shots.
📋 Detailed Metadata: Every clip is paired with structured metadata, including creative descriptions, precise camera movements, lens information, field of view calculations, and shot settings, ensuring AI models can fully understand and replicate real-world cinematography with accuracy.
⚙️ Consistency: Re-thinking training data at the point of capture by "overshooting" a subject, enabling models to learn more nuanced relationships and views across scenes.
🌅 Light: Shot during early morning and sunset light for optimal color contrast and dynamic range, maximizing visual quality for color and lighting-sensitive tasks.
🔍 Curation: Curated specifically for machine learning, providing clean, high-quality data for next generation model training.
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INTRODUCING Hugging Face AutoTrain Client 🔥
Fine-tuning models got even easier!!!!
Now you can fine-tune SOTA models on all compatible dataset-model pairs on Hugging Face Hub using Python on Hugging Face Servers. Choose from a number of GPU flavors, millions of models and dataset pairs and 10+ tasks 🤗
To try, install autotrain-advanced using pip. You can ignore dependencies and install without --no-deps and then you'd need to install some dependencies by hand.
"pip install autotrain-advanced"
Github repo: https://github.com/huggingface/autotrain-advanced
Fine-tuning models got even easier!!!!
Now you can fine-tune SOTA models on all compatible dataset-model pairs on Hugging Face Hub using Python on Hugging Face Servers. Choose from a number of GPU flavors, millions of models and dataset pairs and 10+ tasks 🤗
To try, install autotrain-advanced using pip. You can ignore dependencies and install without --no-deps and then you'd need to install some dependencies by hand.
"pip install autotrain-advanced"
Github repo: https://github.com/huggingface/autotrain-advanced
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