diff --git a/datasets/market.py b/datasets/market.py
index 9b8b1e0..ad610d4 100644
--- a/datasets/market.py
+++ b/datasets/market.py
@@ -40,9 +40,12 @@ def __init__(self, root, image_size, transform=None, loader=default_loader, trai
             old_im_list = glob.glob(os.path.join(self.root, 'train_all', '*/*.png'))
             self.class_dir = glob.glob(os.path.join(self.root, 'train_all', '*'))
         else:
-            old_im_list = glob.glob(os.path.join(self.root, 'query', '*/*.png'))
+            #old_im_list = glob.glob(os.path.join(self.root, 'query', '*/*.png'))
+            old_im_list = sorted(glob.glob(os.path.join(self.root, 'query', '*/*.png')))
             self.class_dir = glob.glob(os.path.join(self.root, 'query', '*'))
-
+         
+            #for index, i in enumerate(old_im_list): 
+            #    print(index, i)
         # threshold
         for index, name in enumerate(old_im_list):
             precentage = float(name[-8:-4])
diff --git a/rainbow/current_mesh_recon.obj b/rainbow/current_mesh_recon.obj
index 58cc276..3b1e9b2 100644
--- a/rainbow/current_mesh_recon.obj
+++ b/rainbow/current_mesh_recon.obj
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diff --git a/rainbow/epoch_400_template.obj b/rainbow/epoch_400_template.obj
deleted file mode 100644
index 8624690..0000000
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diff --git a/show_camera.py b/show_camera.py
index a4acbea..9f124c1 100644
--- a/show_camera.py
+++ b/show_camera.py
@@ -168,17 +168,39 @@ def save_img(output_name):
     selected_index = np.arange(1, 3368, int(3368//opt.batchSize)) 
     print(selected_index) 
     # more challenge cases
-    selected_index[0] = 2415
-    selected_index[10] = 3315
-    selected_index[11] = 2558
-    selected_index[12] = 1175
-    selected_index[14] = 802
-    selected_index[18] = 527
-    selected_index[19] = 1794
-    selected_index[24] = 2520
-    selected_index[25] = 244
-    selected_index[26] = 777 
-    selected_index[28] = 1456
+    selected_index[0] = 2328
+    selected_index[1] = 2614
+    selected_index[2] = 2852
+    selected_index[3] = 2184
+    selected_index[4] = 2476
+    selected_index[5] = 598
+    selected_index[6] = 2354
+    selected_index[7] = 388
+    selected_index[8] = 1300
+    selected_index[9] = 83
+    selected_index[10] = 3273
+    selected_index[11] = 2114
+    selected_index[12] = 2383
+    selected_index[13] = 3100
+    selected_index[14] = 437
+    selected_index[15] = 1082
+    selected_index[16] = 1997
+    selected_index[17] = 2061
+    selected_index[18] = 601
+    selected_index[19] = 1781
+    selected_index[20] = 1236
+    selected_index[21] = 1268
+    selected_index[22] = 2232
+    selected_index[23] = 863
+    selected_index[24] = 3259
+    selected_index[25] = 3026
+    selected_index[26] = 990
+    selected_index[27] = 2582
+    selected_index[28] = 1897
+    selected_index[29] = 1290
+    selected_index[30] = 2333
+    selected_index[31] = 3155
+
     #print(selected_index) 
     train_dataset = MarketDataset(opt.dataroot, opt.imageSize, train=True, threshold=opt.threshold, bg = opt.bg, hmr = opt.hmr)
     test_dataset = MarketDataset(opt.dataroot, opt.imageSize, train=False, threshold=opt.threshold, bg = opt.bg, hmr = opt.hmr, selected_index = selected_index)
diff --git a/show_rainbow2.py b/show_rainbow2.py
index a2a6134..ddcc32c 100644
--- a/show_rainbow2.py
+++ b/show_rainbow2.py
@@ -168,13 +168,39 @@ def save_img(output_name):
     selected_index = np.arange(1, 3368, int(3368//opt.batchSize)) 
     print(selected_index) 
     # more challenge cases
-    selected_index[0] = 2415
-    selected_index[1] = 106
-    selected_index[2] = 2558
-    selected_index[3] = 1458
-    selected_index[4] = 211
-    selected_index[5] = 2101
-    selected_index[6] = 3151
+    selected_index[0] = 2328
+    selected_index[1] = 2614
+    selected_index[2] = 2114
+    selected_index[3] = 1897
+    selected_index[4] = 2852
+    selected_index[5] = 1236
+    selected_index[6] = 2333
+    selected_index[7] = 388
+    selected_index[8] = 1300
+    selected_index[9] = 83
+    selected_index[10] = 2293
+    selected_index[11] = 503
+    selected_index[12] = 1586
+    selected_index[13] = 3100
+    selected_index[14] = 1121
+    selected_index[15] = 1082
+    selected_index[16] = 1997
+    selected_index[17] = 2061
+    selected_index[18] = 77
+    selected_index[19] = 2238 
+    selected_index[20] = 1236
+    selected_index[21] = 1268
+    selected_index[22] = 2232
+    selected_index[23] = 863
+    selected_index[24] = 3258
+    selected_index[25] = 3213
+    selected_index[26] = 555
+    selected_index[27] = 2582
+    selected_index[28] = 867
+    selected_index[29] = 1290
+    selected_index[30] = 2333
+    selected_index[31] = 3155
+
 
     #print(selected_index) 
     train_dataset = MarketDataset(opt.dataroot, opt.imageSize, train=True, threshold=opt.threshold, bg = opt.bg, hmr = opt.hmr)
@@ -209,7 +235,6 @@ def save_img(output_name):
                                          num_workers=int(opt.workers), prefetch_factor=2)
 
 
-
 if __name__ == '__main__':
     # differentiable renderer
     template_file = kal.io.obj.import_mesh(opt.template_path, with_materials=True)
@@ -275,6 +300,7 @@ def save_img(output_name):
     #for i, data in tqdm.tqdm(enumerate(train_dataloader)):
         Xa = Variable(data['data']['images']).cuda()
         paths = data['data']['path']
+        print(paths)
         # Xa = fliplr(Xa)
         with torch.no_grad():
             Ae = netE(Xa)
diff --git a/temp.txt b/temp.txt
new file mode 100644
index 0000000..b94446f
--- /dev/null
+++ b/temp.txt
@@ -0,0 +1,3378 @@
+Random Seed:  0
+Namespace(name='reMKT_wgan_b48_lr0.5_em1_update-1_chf_lpl_reg0.1_data2_m2_flat7_newdepthR0.15_drop0.4_gap2_beta0.9', configs_yml='configs/image.yml', dataroot='../Market/hq/seg_hmr', ratio=2, gan_type='wgan', template_path='./template/ellipsoid.obj', category='bird', pretrain='hr18sv2', norm='bn', workers=4, batchSize=32, imageSize=64, nk=5, nf=32, niter=600, lr=0.0001, clip=0.05, beta1=0.5, droprate=0.4, cuda=1, manualSeed=0, start_epoch=0, warm_epoch=20, multigpus=False, resume=False, chamfer=False, bg=True, nolpl=False, white=False, makeup=0, beta=0, hard=False, L1=False, flipL1=False, coordconv=True, unmask=False, romp=False, swa=False, em=0.0, swa_start=400, update_shape=1, swa_lr=0.0003, lambda_gan=0.0001, ganw=1, lambda_reg=0.1, lambda_edge=0.001, lambda_deform=0.1, lambda_flipz=0.1, lambda_data=1.0, lambda_ic=1, dis1=0, dis2=0, lambda_lc=0, image_weight=1, reg=0.0, em_step=0.1, hmr=0.0, threshold=0.09, bias_range=0.5, azi_scope=360, elev_range='-15~15', hard_range=0, dist_range='2~6', outf='./log/reMKT_wgan_b48_lr0.5_em1_update-1_chf_lpl_reg0.1_data2_m2_flat7_newdepthR0.15_drop0.4_gap2_beta0.9')
+[   1  106  211  316  421  526  631  736  841  946 1051 1156 1261 1366
+ 1471 1576 1681 1786 1891 1996 2101 2206 2311 2416 2521 2626 2731 2836
+ 2941 3046 3151 3256 3361]
+12936 After threshold: 12925
+Succeed loading dataset!
+0 ../Market/hq/seg_hmr/query/0001/0001_c1s1_001051_00.jpg_0.31.png
+1 ../Market/hq/seg_hmr/query/0001/0001_c2s1_000301_00.jpg_0.49.png
+2 ../Market/hq/seg_hmr/query/0001/0001_c3s1_000551_00.jpg_0.24.png
+3 ../Market/hq/seg_hmr/query/0001/0001_c4s6_000810_00.jpg_0.28.png
+4 ../Market/hq/seg_hmr/query/0001/0001_c5s1_001426_00.jpg_0.35.png
+5 ../Market/hq/seg_hmr/query/0001/0001_c6s1_009601_00.jpg_0.26.png
+6 ../Market/hq/seg_hmr/query/0003/0003_c1s6_015971_00.jpg_0.32.png
+7 ../Market/hq/seg_hmr/query/0003/0003_c3s3_064744_00.jpg_0.37.png
+8 ../Market/hq/seg_hmr/query/0003/0003_c4s6_015641_00.jpg_0.33.png
+9 ../Market/hq/seg_hmr/query/0003/0003_c5s3_065187_00.jpg_0.30.png
+10 ../Market/hq/seg_hmr/query/0003/0003_c6s3_088392_00.jpg_0.28.png
+11 ../Market/hq/seg_hmr/query/0004/0004_c1s6_016996_00.jpg_0.27.png
+12 ../Market/hq/seg_hmr/query/0004/0004_c2s3_059152_00.jpg_0.34.png
+13 ../Market/hq/seg_hmr/query/0004/0004_c3s3_065619_00.jpg_0.30.png
+14 ../Market/hq/seg_hmr/query/0004/0004_c5s3_066212_00.jpg_0.28.png
+15 ../Market/hq/seg_hmr/query/0004/0004_c6s3_089492_00.jpg_0.34.png
+16 ../Market/hq/seg_hmr/query/0005/0005_c1s1_001351_00.jpg_0.31.png
+17 ../Market/hq/seg_hmr/query/0005/0005_c2s1_000976_00.jpg_0.28.png
+18 ../Market/hq/seg_hmr/query/0005/0005_c3s1_000826_00.jpg_0.31.png
+19 ../Market/hq/seg_hmr/query/0005/0005_c4s1_006951_00.jpg_0.33.png
+20 ../Market/hq/seg_hmr/query/0005/0005_c5s1_000401_00.jpg_0.33.png
+21 ../Market/hq/seg_hmr/query/0005/0005_c6s1_004576_00.jpg_0.30.png
+22 ../Market/hq/seg_hmr/query/0006/0006_c1s6_026921_00.jpg_0.40.png
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+3368 After threshold: 3368
+Succeed loading dataset!
+Market-1501