Comments (4)
Sorry, I didn't understand your question. You may use Chinese to describe your question and I can translate it into English so that everyone else would still understand.
Sorry for late reply, have a nice day!
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感谢提问,抱歉回复的有些慢,最近太忙了 T T
以 MS-COCO 数据集为例,训练的指令是 ./scripts/run_experiments_coco.sh
,其实Line9-24是base-training stage,Line48-67是few-shot-fintuning stage。Line48-67的命令结束后将自动执行few-shot inference。
如果要单独在novel classes上执行 inference ,直接可以执行:
python -u main.py \
--dataset_file coco_base \
--backbone resnet101 \
--num_feature_levels 1 \
--enc_layers 6 \
--dec_layers 6 \
--hidden_dim 256 \
--num_queries 300 \
--batch_size 2 \
--category_codes_cls_loss \
--resume path/to/checkpoint.pth/generated/by/few-shot-fintuning \
--fewshot_finetune \
--fewshot_seed ${fewshot_seed} \
--num_shots ${num_shot} \
--eval \
2>&1 | tee ${FS_FT_DIR}/log_inference.txt
其中加粗部分为需要需要特别注意的指令。希望可以帮到你!
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Question: After training, how to perform inference?
Answer:
Let's take MS-COCO as an example. The training & inference scripts are in ./scripts/run_experiments_coco.sh
, of which Lines 9-24 refer to "base-training stage", and Lines 48-67 refer to "few-shot-finetuning stage". Lines 48-67 will also perform inference after finetuning is done.
If user wishes to perform inference, simply run:
python -u main.py \
--dataset_file coco_base \
--backbone resnet101 \
--num_feature_levels 1 \
--enc_layers 6 \
--dec_layers 6 \
--hidden_dim 256 \
--num_queries 300 \
--batch_size 2 \
--category_codes_cls_loss \
--resume path/to/checkpoint.pth/generated/by/few-shot-fintuning \
--fewshot_finetune \
--fewshot_seed ${fewshot_seed} \
--num_shots ${num_shot} \
--eval \
2>&1 | tee ${FS_FT_DIR}/log_inference.txt
Note that user should set --eval
and --resume path/to/checkpoint.pth/generated/by/few-shot-fintuning
correctly.
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Related Issues (20)
- coco fine-tuning parameters
- Can you provide the t-SNE visualization code about mmdet? HOT 3
- Is the results of multi-scale version better and why not use it? HOT 1
- Some questions about t-SNE HOT 1
- There was a problem trying to train the code.
- How to evaluate the base training performance?
- split few-shot
- could you improve the training efficiency?
- Could you provide the fine-tuned weights? HOT 1
- About visualize the results.
- How long does it take Meta-Finetuning to converge?
- Performance of Meta-DETR without meta-finetuning? HOT 7
- Some questions about QSAttn. HOT 8
- 训练自己的数据集 HOT 2
- 在训练自己的数据集时,类别数报错。 HOT 2
- Questions about Task Encodings, Class Prototypes, and Category Codes
- How to generate my own few_shot file just as "coco_fewshot" when finetune on custom dataset? HOT 1
- 您好,请问可以公开一下论文中可视化结果的相关代码吗? HOT 1
- Fine-tuning time HOT 1
- Performing inference with CPU HOT 1
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