Llama Thunderdome Clip Aerial Vit B16 v3 Drone Fleet

--- language: - en license: apache-2.0 library_name: open_clip tags: - clip - vision - aerial - drone - tracking - re-identification pipeline_tag: zero-shot-image-classification base_model: openai/clip-vit-base-patch16 --- # llama-thunderdome-clip-aerial-vit-b16-v3-drone-fleet A drone-fleet-specialized fine-tune of OpenCLIP **ViT-B/16** for aerial vehicle + person retrieval and re-identification. ## What this is `v3-drone-fleet` is the **third** member of the `llama-thunderdome-clip-aerial-*` family. It complements `v2`: * `v2` — fine-tuned with **subtype-aware captions** (sedan/SUV/pickup/ school_bus/etc) on curated aerial imagery. Best for **specific vehicle subtype** queries. * **`v3-drone-fleet`** (this model) — fine-tuned on **real drone-fleet footage** (2 drones, 12 videos, 4,575 Gemini-captioned crops). Best for **drone-fleet domain adaptation** — improves discrimination on the actual camera profile + altitude of operational drones. ## Training data | Source | Crops | Captioning | |---|---:|---| | Real drone footage (2 drone profiles, 12 videos) | 4,575 | Gemini-2.5-flash-lite verdict + subtype + color/clothing | Class distribution: person 3,130 · car 1,089 · truck 356. Caption style: `overhead drone view of <color> <subtype>` for vehicles, `person in <clothing_color> clothing` for persons. ## Training recipe ``` base_model: ViT-B-16 pretrained: openai text_encoder: frozen epochs: 15 (best at epoch 4) batch_size: 64 learning_rate: 1e-5 warmup: 100 steps val_split: 0.1 ``` ## Eval results | Metric | Baseline (openai) | v3 best (epoch 4) | Delta | |---|---:|---:|---:| | `val_loss` | 2.83 | **2.74** | -3.2% | | `val_recall@1` (image→text) | 0.169 | **0.212** | +25.4% (rel) | ## How to use ### As a regular open_clip model ```python import open_clip from huggingface_hub import hf_hub_download import torch ckpt_path = hf_hub_download( repo_id="llama-farm/llama-thunderdome-clip-aerial-vit-b16-v3-drone-fleet", filename="best.pt" ) model, _, preprocess = open_clip.create_model_and_transforms( "ViT-B-16", pretrained="openai" ) state = torch.load(ckpt_path, map_location="cpu") model.load_state_dict(state["model"] if "model" in state else state) model.eval() ``` ### As a ReID backbone inside `llama-thunderdome` (BotSORT / DeepOcSort) ```python from thunderdome.tracking.clip_reid_adapter import CLIPReidAdapter adapter = CLIPReid...