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A deep learning code base, mainly for paper replication, in the areas of image recognition, object detection, image segmentation, self-supervision, etc. Each project can be run independently, and there are corresponding articles to explain.
License: MIT License
Python 94.51%
Dockerfile 0.07%
Shell 0.17%
C++ 3.48%
CMake 0.05%
Java 0.23%
Makefile 0.02%
Jupyter Notebook 1.23%
Cuda 0.25%
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您好作者,我在用TransFG跑IP102数据集的时候,在训练阶段loss一直保持在4点多不变,Valid Accuracy只有百分之0.4,经过多轮训练一直不变化,请问您有遇到过这类的问题么?
Hi,thanks a lot for publishing the code. I had a question: I use your code to train CUB_200_2011datasets, but loss does not decrease while training
使用vggnet,有多块GPU怎么指定具体某一块?
你好,请问你们是否尝试过在斯坦福汽车数据集上测试代码,我用源码跑下来效果一直很差,比论文中的效果低3%左右
转了之后不知道怎么配置datasets下自定义的.yaml文件,
names:
0: Drosicha_contrahens_female
1: Drosicha_contrahens_male
2: Chalcophora_japonica
3: Anoplophora_chinensis
4: Psacothea_hilaris(Pascoe)
5: Apriona_germari(Hope)
6: Monochamus_alternatus
7: Plagiodera_versicolora(Laicharting)
8: Latoia_consocia_Walker
9: Hyphantria_cunea
10: Cnidocampa_flavescens(Walker)
11: Cnidocampa_flavescens(Walker_pupa)
......
不知道怎么能抽取出来,然后运行会报错
Traceback (most recent call last):
File "train.py", line 8, in
model.train(data="./ultralytics/cfg/datasets/pest.yaml", epochs=3) # train the model
File "/root/autodl-tmp/ultralytics-main/ultralytics/engine/model.py", line 336, in train
self.trainer = (trainer or self._smart_load('trainer'))(overrides=args, _callbacks=self.callbacks)
File "/root/autodl-tmp/ultralytics-main/ultralytics/engine/trainer.py", line 123, in init
raise RuntimeError(emojis(f"Dataset '{clean_url(self.args.data)}' error ❌ {e}")) from e
RuntimeError: Dataset 'ultralytics/cfg/datasets/pest.yaml' error ❌ object of type 'NoneType' has no len()