Run 2A checkpoint (RF-DETR-S, rfdetr 1.11.0) and model card

README.md

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+---
+license: apache-2.0
+library_name: rfdetr
+pipeline_tag: object-detection
+tags:
+  - object-detection
+  - rf-detr
+  - computer-vision
+---
+
+# Weightlifting-plate detector (RF-DETR-S, Run 2A)
+
+An 11-class object detector for weightlifting plates in side-on photos of a loaded barbell
+sleeve: the ten plate weights from 0.5 kg to 25 kg, plus the collar. It is RF-DETR-S fine-tuned
+with `rfdetr` 1.11.0. This is the **Run 2A** checkpoint, the one the project ships.
+
+Code, dataset audit, pre-registered protocols and every number on this page:
+https://github.com/Prithv122/custom-detector
+
+## Files
+
+| File | Size (bytes) | SHA-256 |
+|---|--:|---|
+| `checkpoint_best_total.pth` | 127,623,409 | `f802956130702712f05bfb82d8b437ea1648efe642ff90bbba2fb38a1d8bce1f` |
+
+## How to use
+
+```python
+from huggingface_hub import hf_hub_download
+from PIL import Image
+from rfdetr import RFDETR
+
+path = hf_hub_download("Prithv122/custom-detector", "checkpoint_best_total.pth")
+model = RFDETR.from_checkpoint(path, trust_checkpoint=True, device="cpu")
+det = model.predict(Image.open("sleeve.jpg").convert("RGB"), threshold=0.53)
+for box, conf, name in zip(det.xyxy, det.confidence, det.data["class_name"]):
+    print(name, round(float(conf), 3), box.round(1))
+```
+
+`trust_checkpoint=True` loads a pickled PyTorch file, so only load it from a source you trust.
+Pin a `revision` to get the same bytes every time. The 0.53 confidence threshold is the one the
+project picked for this checkpoint on the validation split, with a rule fixed before training.
+
+## Model
+
+| | |
+|---|---|
+| Architecture | RF-DETR-S (`RFDETRSmall`: DINOv2 windowed-small encoder), input resolution 512 |
+| Library | `rfdetr` 1.11.0 (Apache-2.0) |
+| Classes (11) | `0.5kg` `1kg` `1.5kg` `2kg` `2.5kg` `5kg` `10kg` `15kg` `20kg` `25kg` `zacisk` (collar) |
+| Training | Tesla T4 on Kaggle, 137 min, torch 2.11.0+cu128. 50-epoch budget, early stopping (patience 10), batch 8 × 2 gradient-accumulation steps, lr 1e-4, seed 20261001. Best checkpoint selected on val by `rfdetr` |
+| Augmentation | horizontal flip (p = 0.5) + `PlanckianJitter` (CIE D series, 4000–15000 K, p = 0.5), albumentations 2.0.8 |
+| Training code | commit `4b09e46` of the GitHub repo |
+
+## Training data
+
+[*Weightlifting Plates*](https://universe.roboflow.com/projektciezary/weightlifting-plates) by
+ProjektCiezary, Roboflow Universe, version 10, **CC BY 4.0**. Changes made: 255 of 2,251 images
+were removed (third-party competition-video crops and press photos, unlabelled images,
+contradictory labels), leaving 1,996. The published split leaked near-duplicate photos across
+splits, so it was replaced: train 1,263, val 331 (whole capture sessions), test 402 (one whole
+capture date, 2025-05-04, never used for training). The images are side-on phone photos of one
+loaded sleeve, mostly coloured bumper plates on a loading rig, taken on 7 dates between May and
+November 2025.
+
+## Why this checkpoint
+
+Run 1 (no colour augmentation) misread 40 of the 100 test-date 1.5 kg plates as 0.5 kg. Those
+misreads all sat in front of a yellow backdrop, and recolouring the whole scene around them
+flipped 24 of the 40 to a correct read. Run 2 then trained two arms on one commit, with one seed
+each, and scored them by a rule written before training:
+
+- **2C (control):** horizontal flip only.
+- **2A (shipped):** horizontal flip plus a colour-temperature cast (`PlanckianJitter`).
+
+Results on the 100 test 1.5 kg boxes (each arm at its own val-chosen threshold):
+
+| Run | Threshold | Read as 1.5 kg (correct) | Read as 0.5 kg (the known failure) | Missed | Other |
+|---|--:|--:|--:|--:|--:|
+| Run 1 (reference) | 0.80 | 36 | 40 | 21 | 3 |
+| 2C control | 0.67 | 40 | 51 | 6 | 3 |
+| **2A shipped** | 0.53 | **72** | **25** | 0 | 3 |
+
+- **Known failure, 1.5 kg read as 0.5 kg:** 51/100 (2C) → **25/100** (2A).
+- **Correct 1.5 kg reads:** 40/100 (2C) → **72/100** (2A).
+- **Test mAP@50–95:** 0.748 (2C) → **0.762** (2A). Val mAP@50–95: 0.823 → 0.821. No other
+  class's test AP@50–95 dropped by more than 0.011 (the limit was 0.05).
+- **Pre-registered verdict: fixed.** The success criterion was that 2A misreads at most half as
+  many as 2C, rounded down: at most 25. 2A misread exactly 25, so **the criterion was met
+  exactly at the boundary**. One more misread and the verdict would have been *not fixed*.
+- Paired on the same 100 boxes, 26 were misread by 2C only and none by 2A only (one-sided exact
+  McNemar, p = 1.5e-8).
+
+## Limitations
+
+Read these before quoting any number above.
+
+- **The test date had already been used for diagnosis.** Its errors chose the question, two
+  analyses measured its images, and the fix is scored on it. Run 2's numbers show that the known
+  failure is reduced on that date, not how the model does on a new day.
+- **Run 1's untouched-date test mAP@50–95 of 0.741 remains the clean new-day generalisation
+  estimate.** It was measured before any of the diagnosis. The dataset has no untouched date
+  left, so this checkpoint has no clean new-day number of its own.
+- **One seed per arm.** McNemar's test covers which boxes happen to be in the test set, not
+  training randomness. Retraining without the colour change moved the misread count from 40
+  (Run 1) to 51 (2C), which gives a sense of how much one retrain can move it.
+- **Augmentation success does not prove lighting caused the original failure.** The cast
+  simulates the colour of the light and shifts plate and backdrop together. That a cast helps
+  supports "robustness to scene colour helps here". It does not show that white balance or
+  illumination caused the failure on that day.
+- **This is not a general colour or lighting robustness claim.** The evidence is one failure
+  mode on one capture date. 25 of 100 test 1.5 kg plates are still read as 0.5 kg, all of them
+  in front of the yellow backdrop.
+- **The arms used different confidence thresholds** (2C 0.67, 2A 0.53), each picked on that
+  arm's own val split by a rule fixed before training. At 0.53, 2A makes 12 test predictions that
+  match no labelled box (2C makes none). No equal-threshold comparison was pre-registered or run.
+- **Narrow domain.** Side-on close-ups of one sleeve, from one dataset's phone photos on 7 dates.
+  It has not been tested on other gyms, plate brands, camera angles or full-bar shots.
+- Val shares capture dates with train, so val scores are optimistic by design.
+
+## Licence and attribution
+
+The weights are released under Apache-2.0, the licence of the RF-DETR-S base model. They were
+trained on *Weightlifting Plates* by ProjektCiezary (Roboflow Universe, CC BY 4.0), filtered and
+re-split as described above.

checkpoint_best_total.pth

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