From ca826a45057d61736d5207735f6357e4fd1505d0 Mon Sep 17 00:00:00 2001 From: heyufan Date: Mon, 18 May 2026 16:16:19 -0400 Subject: [PATCH] Fix finetune doc link bug Signed-off-by: heyufan --- NV-Segment-CT/docs/README.md | 4 ++-- NV-Segment-CTMR/docs/README.md | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/NV-Segment-CT/docs/README.md b/NV-Segment-CT/docs/README.md index d323313..7748968 100644 --- a/NV-Segment-CT/docs/README.md +++ b/NV-Segment-CT/docs/README.md @@ -16,7 +16,7 @@ cd NV-Segment-CTMR/NV-Segment-CT; pip install -r requirements.txt; ``` -Model weights are prepared automatically during inference. The first run downloads the checkpoint from Hugging Face into the local Hugging Face cache and links it at `models/model.pt`; later runs reuse the cached weights while still touching Hugging Face download stats for each inference. +Model weights are prepared automatically during inference. The first run downloads the checkpoint from Hugging Face into the local Hugging Face cache and links it at `models/model.pt`; ## 1.1 **NV-Segment-CT** [[Github]](https://github.com/NVIDIA-Medtech/NV-Segment-CTMR/tree/main/NV-Segment-CT) [[Huggingface]](https://huggingface.co/nvidia/NV-Segment-CT) @@ -64,7 +64,7 @@ For more details, please refer to [this](inference.md). ## Continual learning / Finetuning -We provide predefined finetuning tutorial in [details](inference.md). +We provide predefined finetuning tutorial in [details](finetune.md). For complicated finetuning, we suggest users to do vibe coding to generate finetuning pipelines by simply reuse the model and checkpoint ```python diff --git a/NV-Segment-CTMR/docs/README.md b/NV-Segment-CTMR/docs/README.md index 9658921..56f3da1 100644 --- a/NV-Segment-CTMR/docs/README.md +++ b/NV-Segment-CTMR/docs/README.md @@ -33,7 +33,7 @@ cd NV-Segment-CTMR/NV-Segment-CTMR pip install -r requirements.txt ``` -Model weights are prepared automatically during inference. The first run downloads the checkpoint from Hugging Face into the local Hugging Face cache and links it at `models/model.pt`; later runs reuse the cached weights while still touching Hugging Face download stats for each inference. +Model weights are prepared automatically during inference. The first run downloads the checkpoint from Hugging Face into the local Hugging Face cache and links it at `models/model.pt`; ## Automatic Segmentation (support multi-gpu batch processing) @@ -211,7 +211,7 @@ python -m monai.bundle run --config_file "['configs/inference.json', 'configs/in ## Continual learning / Finetuning -We provide predefined finetuning tutorial in [details](inference.md). +We provide predefined finetuning tutorial in [details](finetune.md). For complicated finetuning, we suggest users to do vibe coding to generate finetuning pipelines by simply reuse the model and checkpoint ```python