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View Code? Open in Web Editor NEW[DeepFashion2 Challenge] Fashion Landmark Estimation with HRNet
License: MIT License
[DeepFashion2 Challenge] Fashion Landmark Estimation with HRNet
License: MIT License
Nice work! Did you calculate sigmas (
) by yourself?i just want to know how can we apply the entire repo on another dataset
I test the checkpoint, using the command in Readme.
python tools/test.py
--cfg experiments/deepfashion2/hrnet/w48_384x288_adam_lr1e-3.yaml
TEST.MODEL_FILE models/pose_hrnet-w48_384x288-deepfashion2_mAP_0.7017.pth
TEST.USE_GT_BBOX True
I open the debug switch in config file, and the saved image result is very bad. Any show problem?
训练的时候出现以下错误
for catId in catIds}
File "/home/sa/anaconda3/envs/torch1.7/lib/python3.6/site-packages/pycocotools/cocoeval.py", line 229, in computeOks
e = (dx2 + dy2) / vars / (gt['area']+np.spacing(1)) / 2
ValueError: operands could not be broadcast together with shapes (294,) (17,)
@ShenhanQian
how can we get clothes bbox, clothes class, scale and other parameters, if we want to run this repo on test image
Thanks for sharing great job! However, when configuring the dataset, I cannot find bounding box results named 'bbox_result_val.pkl' in the provided OneDrive link. Did I miss anything?
I am aiming to create an inference pipeline for images in the wild. One of the problem that I am facing is to detect bboxs. Can you please upload the model and code for your detector? Otherwise there's also this repo that I found that can be used for this purpose: https://github.com/archana1998/Clothing-Detection
First, thanks for sharing this great work! Here is some issue that I met.
I try to visualize the result by running the script###
python tools/test.py --cfg experiments/deepfashion2/hrnet/w48_384x288_adam_lr1e-3.yaml TEST.MODEL_FILE models/pose_hrnet-w48_384x288-deepfashion2_mAP_0.7017.pth TEST.USE_GT_BBOX True DATASET.MINI_DATASET True TAG 'experiment description' WORKERS 4 TEST.BATCH_SIZE_PER_GPU 8 TRAIN.BATCH_SIZE_PER_GPU 8
the config file is
AUTO_RESUME: false #
CUDNN:
BENCHMARK: true
DETERMINISTIC: false
ENABLED: true
DATA_DIR: ''
GPUS: (1,)
OUTPUT_DIR: 'output'
LOG_DIR: 'log'
WORKERS: 8
PRINT_FREQ: 100
PIN_MEMORY: true
DATASET:
COLOR_RGB: false
DATASET: 'deepfashion2'
DATA_FORMAT: jpg
FLIP: true
NUM_JOINTS_HALF_BODY: 8
PROB_HALF_BODY: 0.3
ROOT: 'data/deepfashion2/'
ROT_FACTOR: 15 #45
SCALE_FACTOR: 0.1 #0.35
TEST_SET: 'validation'
TRAIN_SET: 'train'
MINI_DATASET: True
SELECT_CAT: [1,2,3,4,5,6,7,8,9,10,11,12,13]
MODEL:
INIT_WEIGHTS: true
NAME: pose_hrnet
NUM_JOINTS: 294
PRETRAINED: ''
TARGET_TYPE: gaussian
IMAGE_SIZE:
I change the CONFIG parameter to True, however it still does not save any image. The image saving only works when I change the BATH_SIZE_PER_GPU to 1. However, the image-saving function is based on a torch grid, thus result in a very wired visualization since the scale of keypoint and output is different. Could you please try to solve the problem? I am using a single GPU RTX 3080TI with Ubuntu 18.04.
Thanks for great job! And there is a question about the output of pretained model provided.
When I test pretained model, it return a tensor whose shape is [batchsize, 294, height, weight]. Could you give some expanation about the number "294" ?
First, I would like to thank authors for sharing the code. The work inspired me a lot and I‘m trying to followup your method for finding keypoints using my own input images.
However, I found that it is hard to directly modify test.py to meet a situation when the input is not the validation set, which really confused me. Is there a straightforward way to generate landmark with input images not in the deepfashion2 dataset?
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