diff --git a/eval_metrics_multiprocessing.py b/eval_metrics_multiprocessing.py
new file mode 100755
index 0000000..cf4beb6
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+++ b/eval_metrics_multiprocessing.py
@@ -0,0 +1,161 @@
+from __future__ import print_function, absolute_import
+
+import numpy as np
+import copy
+from collections import defaultdict
+import sys
+import multiprocessing
+
+def eval_cuhk03(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, N=100):
+    """Evaluation with cuhk03 metric
+    Key: one image for each gallery identity is randomly sampled for each query identity.
+    Random sampling is performed N times (default: N=100).
+    """
+    num_q, num_g = distmat.shape
+    if num_g < max_rank:
+        max_rank = num_g
+        print("Note: number of gallery samples is quite small, got {}".format(num_g))
+    indices = np.argsort(distmat, axis=1)
+    matches = (g_pids[indices] == q_pids[:, np.newaxis]).astype(np.int32)
+
+    # compute cmc curve for each query
+    all_cmc = []
+    all_AP = []
+    num_valid_q = 0. # number of valid query
+    for q_idx in range(num_q):
+        # get query pid and camid
+        q_pid = q_pids[q_idx]
+        q_camid = q_camids[q_idx]
+
+        # remove gallery samples that have the same pid and camid with query
+        order = indices[q_idx]
+        remove = (g_pids[order] == q_pid) & (g_camids[order] == q_camid)
+        keep = np.invert(remove)
+
+        # compute cmc curve
+        orig_cmc = matches[q_idx][keep] # binary vector, positions with value 1 are correct matches
+        if not np.any(orig_cmc):
+            # this condition is true when query identity does not appear in gallery
+            continue
+
+        kept_g_pids = g_pids[order][keep]
+        g_pids_dict = defaultdict(list)
+        for idx, pid in enumerate(kept_g_pids):
+            g_pids_dict[pid].append(idx)
+
+        cmc, AP = 0., 0.
+        for repeat_idx in range(N):
+            mask = np.zeros(len(orig_cmc), dtype=np.bool)
+            for _, idxs in g_pids_dict.items():
+                # randomly sample one image for each gallery person
+                rnd_idx = np.random.choice(idxs)
+                mask[rnd_idx] = True
+            masked_orig_cmc = orig_cmc[mask]
+            _cmc = masked_orig_cmc.cumsum()
+            _cmc[_cmc > 1] = 1
+            cmc += _cmc[:max_rank].astype(np.float32)
+            # compute AP
+            num_rel = masked_orig_cmc.sum()
+            tmp_cmc = masked_orig_cmc.cumsum()
+            tmp_cmc = [x / (i+1.) for i, x in enumerate(tmp_cmc)]
+            tmp_cmc = np.asarray(tmp_cmc) * masked_orig_cmc
+            AP += tmp_cmc.sum() / num_rel
+        cmc /= N
+        AP /= N
+        all_cmc.append(cmc)
+        all_AP.append(AP)
+        num_valid_q += 1.
+
+    assert num_valid_q > 0, "Error: all query identities do not appear in gallery"
+
+    all_cmc = np.asarray(all_cmc).astype(np.float32)
+    all_cmc = all_cmc.sum(0) / num_valid_q
+    mAP = np.mean(all_AP)
+
+    return all_cmc, mAP
+
+def compute_cmc_ap(q_idx, q_pids, g_pids, q_camids, g_camids, indices, matches, max_rank, result_dict):
+    """
+    Applicable to eval_market1501()
+    """
+
+    # get query pid and camid
+    q_pid = q_pids[q_idx]
+    q_camid = q_camids[q_idx]
+
+    # remove gallery samples that have the same pid and camid with query
+    order = indices[q_idx]
+    remove = (g_pids[order] == q_pid) & (g_camids[order] == q_camid)
+    keep = np.invert(remove)
+
+    # compute cmc curve
+    orig_cmc = matches[q_idx][keep] # binary vector, positions with value 1 are correct matches
+    if not np.any(orig_cmc):
+        # this condition is true when query identity does not appear in gallery
+        result_dict[str(q_idx) + '_valid'] = False
+        return
+    
+    result_dict[str(q_idx) + '_valid'] = True
+
+    cmc = orig_cmc.cumsum()
+    cmc[cmc > 1] = 1
+
+    result_dict[str(q_idx) + '_cmc'] = cmc[:max_rank]
+
+    # compute average precision
+    # reference: https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Average_precision
+    num_rel = orig_cmc.sum()
+    tmp_cmc = orig_cmc.cumsum()
+    tmp_cmc = [x / (i+1.) for i, x in enumerate(tmp_cmc)]
+    tmp_cmc = np.asarray(tmp_cmc) * orig_cmc
+    AP = tmp_cmc.sum() / num_rel
+    result_dict[str(q_idx) + '_ap'] = AP
+
+def eval_market1501(distmat, q_pids, g_pids, q_camids, g_camids, max_rank):
+    """Evaluation with market1501 metric
+    Key: for each query identity, its gallery images from the same camera view are discarded.
+    Multiprocessing is supported.
+    """
+    num_q, num_g = distmat.shape
+    if num_g < max_rank:
+        max_rank = num_g
+        print("Note: number of gallery samples is quite small, got {}".format(num_g))
+    indices = np.argsort(distmat, axis=1)
+    matches = (g_pids[indices] == q_pids[:, np.newaxis]).astype(np.int32)
+
+    # do multiprocessing
+    manager = multiprocessing.Manager()
+    result_dict = manager.dict()
+
+    jobs = []
+    for q_idx in range(num_q):
+        p = multiprocessing.Process(
+            target=compute_cmc_ap,
+            args=(q_idx, q_pids, g_pids, q_camids, g_camids, indices, matches, max_rank, result_dict),
+        )
+        jobs.append(p)
+        p.start()
+    for proc in jobs:
+        proc.join()
+
+    num_valid_q = 0
+    all_cmc, all_AP = [], []
+    for q_idx in range(num_q):
+        if result_dict[str(q_idx) + '_valid']:
+            num_valid_q += 1
+            all_cmc.append(result_dict[str(q_idx) + '_cmc'])
+            all_AP.append(result_dict[str(q_idx) + '_ap'])
+
+    assert num_valid_q > 0, "Error: all query identities do not appear in gallery"
+
+    all_cmc = np.asarray(all_cmc).astype(np.float32)
+    all_cmc = all_cmc.sum(0) / num_valid_q
+    mAP = np.mean(all_AP)
+
+    return all_cmc, mAP
+
+def evaluate(distmat, q_pids, g_pids, q_camids, g_camids, max_rank=50, use_metric_cuhk03=False):
+    if use_metric_cuhk03:
+        return eval_cuhk03(distmat, q_pids, g_pids, q_camids, g_camids, max_rank)
+    else:
+        return eval_market1501(distmat, q_pids, g_pids, q_camids, g_camids, max_rank)
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