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LICENSE

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+SwiftVit/CNN Restricted Research License - BCS
+
+Copyright (c) 2025 Ballinrobe Community School
+
+PERMISSIONS:
+- Use restricted to Ballinrobe Community School students, faculty, and staff only
+- No commercial use
+- No derivative works
+
+PERMITTED USE:
+- Analysis of swift feeding behavior for climate research
+- BT Young Scientist Exhibition 2025 entry only
+
+Any other use requires explicit written authorization from Ballinrobe Community School.
+Unauthorized use will be prosecuted to the fullest extent of applicable law.
+
+---
+
+AUTOMATIC LICENSE TRANSITION:
+
+Effective January 20, 2026, this work is automatically relicensed under:
+Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
+https://creativecommons.org/licenses/by-sa/4.0/
+
+After this date, anyone may use, modify, and distribute this work under CC BY-NC-SA 4.0 terms.

README.md

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+---
+language:
+- en
+pipeline_tag: video-classification
+tags:
+- birds
+- swifts
+- MViTv2
+- Ballinrobe
+license: other
+license_name: bcs-lcs
+license_link: LICENSE
+base_model:
+- timm/mvitv2_small.fb_in1k
+library_name: transformers
+datasets:
+- odinglynn/swift-150
+---
+
+# SwiftViT-150
+
+MViT-v2 fine-tuned on 150 videos for common swift feeding behavior classification.
+
+## Model
+
+Fine-tuned `mvit_v2_s` (Kinetics-400 pretrained) on single-camera nestbox footage. Achieves ~87% validation accuracy (in controlled settings) and demonstrates surprising cross-camera generalization despite training on a single viewpoint and on a miniscule dataset (150 samples).
+
+## Usage
+```python
+import torch
+import torchvision
+
+model = torchvision.models.video.mvit_v2_s(weights=None)
+model.head = torch.nn.Sequential(
+    torch.nn.Dropout(0.5),
+    torch.nn.Linear(768, 512),
+    torch.nn.GELU(),
+    torch.nn.Dropout(0.3),
+    torch.nn.Linear(512, 3),
+)
+
+checkpoint = torch.load("swiftvit-150.pth")
+model.load_state_dict(checkpoint["model_state_dict"])
+model.eval()
+
+# Inference
+with torch.no_grad():
+    video = load_video()  # Shape: [C, T, H, W]
+    output = model(video.unsqueeze(0))
+    prediction = torch.argmax(output, dim=1)
+    # 0: feeding, 1: possible_feeding, 2: not_feeding
+```
+
+## Architecture
+
+- Base: MViT-v2 Small (24M params)
+- Head: Custom 768→512→3 with dropout
+- Input: 16 frames @ 224x224
+- Classes: 3 (feeding, possible_feeding, not_feeding)
+
+## Training
+
+- 120 train / 30 val samples
+- Batch size: 4
+- Optimizer: AdamW (lr=1e-4, wd=0.05)
+- Scheduler: CosineAnnealingWarmRestarts
+- Mixed precision training on H100
+- Early stopping: 40 epoch patience
+
+## Performance
+
+- Train accuracy: 100%
+- Val accuracy: 87%
+- Unexpected cross-camera generalization observed
+
+## Dataset
+
+Trained on [swift-150](https://huggingface.co/datasets/odinglynn/swift-150) - 150 videos from GABLE nestbox camera (Ireland, 2020-2025).
+
+## Context
+
+Part of climate research correlating swift feeding patterns with weather data at terrabyte scale. Ballinrobe Community School entry for REDACTED.
+
+## Citation
+
+If you reference this work, cite:
+```bibtex
+@misc{swift150bcs,
+  title={Swift-150: A Dataset for Common Swift Feeding Behavior Analysis},
+  author={Odin Glynn-Martin, Culan O'Meara, Anas Rashid, Shayden D'Souza, Pádraig Foley and Mark Lally},
+  year={2025},
+  institution={Ballinrobe Community School},
+  url={https://ballinrobecommunityschool.ie},
+  note={REDACTED - Entry 2025}
+}
+```
+
+## License
+
+Proprietary. See LICENSE for restrictions.

swiftvit-150.pth

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