Questions about Train

[wanan0414]

I'm going to do a radar signal classification task, and using the pre-training weights from imagenet isn't appropriate. I also don't have enough dataset for pre-training here, is it possible to train directly with supervised learning using only a training set with 22000 data volume and a test set? Looking forward to your answer!

[ahatamiz]

Hi @wanan0414

Yes ! the codebase supports training on any datasets for classification tasks. Here's a command prompt assuming you are running on 8 GPUs (but it is also easy to extend for multi-node setup). You need to specify DATA_PATH_TRAIN and DATA_PATH_VAL for your train and validation (test) sets, respectively.

Although I recommend trying different learning rates LR and batch sizes BS for your setup.

Needless to say, any of our MambaVision variants can be specified such as mamba_vision_T.

#!/bin/bash

MODEL=mamba_vision_T
DATA_PATH_TRAIN="/my_dataset/train"
DATA_PATH_VAL="/my_dataset/val"
BS=256
EXP=my_experiment
LR=5e-4
WD=0.05
DR=0.2

torchrun --nproc_per_node=8 train.py --input-size 3 224 224 --crop-pct=0.875 \
--train-split=$DATA_PATH_TRAIN --val-split=$DATA_PATH_VAL --model $MODEL --amp --weight-decay ${WD} --drop-path ${DR} --batch-size $BS --tag $EXP --lr $LR

Hope this helps but let me know if there's any issue.

[ahatamiz]

The above also assumes that images of each class are placed within the same folder under both train or validation as shown in the following:

├── train
│   ├── class1
│   │   ├── img1.jpeg
│   │   ├── img2.jpeg
│   │   └── ...
│   ├── class2
│   │   ├── img3.jpeg
│   │   └── ...
│   └── ...
└── val
    ├── class1
    │   ├── img4.jpeg
    │   ├── img5.jpeg
    │   └── ...
    ├── class2
    │   ├── img6.jpeg
    │   └── ...
    └── ...

[wanan0414]

Hi @ahatamiz Thank you very much for your reply! My dataset is in the same format as the train-val you mentioned. Thank you for providing the schematic code. A new question I have is in that case do I not use validate.py and just focus on the train.py file?

[ahatamiz]

Hi @wanan0414

In this case, validate.py can be used to test the model's performance on your test set, assuming it's different from your validation set.