Published `best_model_avg.pth` does not reproduce the hold-short IoU in Table 3
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Valutazione
- Difficoltà
- 4/5
- Tempo stimato
- 3-5 giorni
- Idoneità per principianti
- 48/100
- Tipo di issue
- Bug
- Chiarezza
- Abbastanza chiara
- Stato di attività
- Tranquilla
- Stack tecnologico
- huggingface, python
- Ambito
- computer-vision, machine-learning
Direzione di ricerca
Start by reproducing the reported validation results with best_model_avg.pth from the Hugging Face repository and compare them with Table 3, focusing on hold-short IoU. Read the training code around best_model_avg.pth and best_model_worstcase.pth to determine which checkpoint produced the table. Done means identifying and documenting the checkpoint or evaluation difference that explains the mismatch.
Scritto dal modello di indicizzazione a partire dal testo della issue.
Descrizione
Hi,
My name is Henry and I'm a rising senior in high school. This summer I reproduced the segmentation stage of your taxiing localization paper and I ran into something I couldn't figure out on my own and wanted to ask about.
Using best_model_avg.pth from your Hugging Face repo on the shipped validation split, I get centerline 0.7275 and pavement 0.9824 which are both within half a percent of Table 3. My confusion was because hold-short comes out at 0.3505 instead of 0.6645.
I checked whether it was the decision threshold, the split, or how IoU gets averaged, and none of those explain it. The checkpoint is only finding about 40% of the hold-short pixels.
Was Table 3 made with this checkpoint, or a different one? I noticed the training code also saves best_model_worstcase.pth, which isn't on Hugging Face.
Happy to send the full numbers if that's useful. Thanks for releasing the code
and the dataset, I really enjoyed the reading and the opportunity to give it a try.
Best,
Henry
- Lingua principale
- Python
- Stelle
- 0
- Fork
- 1
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