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README.md

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+---
+tags:
+- image-feature-extraction
+- timm
+- vision-transformer
+- moco-v3
+- self-supervised-learning
+- precision-at-scale
+library_name: timm
+license: cc-by-nc-4.0
+datasets:
+- jesusmolrdv/pas-i
+---
+
+# mocov3-vitb16-pas-insects
+
+MoCo-v3 ViT-B/16 backbone, self-supervised pretrained from scratch on the
+`PaS-I` domain-specific dataset (Insects, ~1.9M images) generated by
+the Precision at Scale pipeline.
+
+## Training data
+
+Pretrained on [`jesusmolrdv/pas-i`](https://huggingface.co/datasets/jesusmolrdv/pas-i),
+the Precision at Scale domain-specific dataset for this checkpoint.
+
+## Paper
+
+- **Paper (Pattern Recognition, 2026):** https://doi.org/10.1016/j.patcog.2025.112236
+- **Official code:** https://github.com/jesusmolrdv/Precision-at-Scale
+
+```bibtex
+@article{rodriguezdevera2026precision,
+  title   = {Precision at scale: Domain-specific datasets on-demand},
+  author  = {Rodr{\'i}guez-de-Vera, Jes{\'u}s M. and Estepa, Imanol G. and Saras{\'u}a, Ignacio and Nagarajan, Bhalaji and Radeva, Petia},
+  journal = {Pattern Recognition},
+  volume  = {171},
+  pages   = {112236},
+  year    = {2026},
+  publisher = {Elsevier},
+  doi     = {10.1016/j.patcog.2025.112236}
+}
+```
+
+
+<details>
+<summary>arXiv preprint</summary>
+
+https://arxiv.org/abs/2407.03463
+
+</details>
+
+## Usage
+
+This backbone is stored in plain [`timm`](https://github.com/huggingface/pytorch-image-models)
+format (`vit_base_patch16_224`, `num_classes=0`), so it loads directly
+through the `timm` Hugging Face Hub integration:
+
+```python
+import timm
+
+model = timm.create_model("hf_hub:jesusmolrdv/mocov3-vitb16-pas-insects", pretrained=True)
+model.eval()
+```
+
+`model` outputs 768-d backbone features (no classification head attached).
+
+

checkpoint.ckpt

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config.json

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+{
+  "architecture": "vit_base_patch16_224",
+  "url": "",
+  "custom_load": false,
+  "num_classes": 0,
+  "num_features": 768,
+  "input_size": [
+    3,
+    224,
+    224
+  ],
+  "pool_size": null,
+  "crop_pct": 0.875,
+  "interpolation": "bicubic",
+  "fixed_input_size": true,
+  "mean": [
+    0.485,
+    0.456,
+    0.406
+  ],
+  "std": [
+    0.229,
+    0.224,
+    0.225
+  ],
+  "first_conv": "patch_embed.proj",
+  "classifier": "head",
+  "labels": []
+}

pytorch_model.bin

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