diff --git a/include/darknet.h b/include/darknet.h
index 00b49921f52..55f94ac5224 100644
--- a/include/darknet.h
+++ b/include/darknet.h
@@ -102,7 +102,7 @@ typedef struct tree {
 
 // activations.h
 typedef enum {
-    LOGISTIC, RELU, RELIE, LINEAR, RAMP, TANH, PLSE, LEAKY, ELU, LOGGY, STAIR, HARDTAN, LHTAN, SELU, SWISH
+    LOGISTIC, RELU, RELIE, LINEAR, RAMP, TANH, PLSE, LEAKY, ELU, LOGGY, STAIR, HARDTAN, LHTAN, SELU, SWISH, MISH
 }ACTIVATION;
 
 // parser.h
@@ -347,7 +347,7 @@ struct layer {
     float *col_image;
     float * delta;
     float * output;
-    float * output_sigmoid;
+    float * activation_input;
     int delta_pinned;
     int output_pinned;
     float * loss;
@@ -532,7 +532,7 @@ struct layer {
 
     float * input_antialiasing_gpu;
     float * output_gpu;
-    float * output_sigmoid_gpu;
+    float * activation_input_gpu;
     float * loss_gpu;
     float * delta_gpu;
     float * rand_gpu;
diff --git a/src/activation_kernels.cu b/src/activation_kernels.cu
index 24563c69d6e..846c586fada 100644
--- a/src/activation_kernels.cu
+++ b/src/activation_kernels.cu
@@ -199,6 +199,16 @@ __global__ void activate_array_swish_kernel(float *x, int n, float *output_sigmo
     }
 }
 
+__global__ void activate_array_mish_kernel(float *x, int n, float *activation_input, float *output_gpu)
+{
+    int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if (i < n) {
+        float x_val = x[i];
+        activation_input[i] = x_val;    // store value before activation
+        output_gpu[i] = x_val * tanh_activate_kernel(log(1 + expf(x_val)));
+    }
+}
+
 __global__ void activate_array_leaky_kernel(float *x, int n)
 {
     int index = blockIdx.x*blockDim.x + threadIdx.x;
@@ -263,6 +273,18 @@ __global__ void gradient_array_swish_kernel(float *x, int n, float *sigmoid_gpu,
     }
 }
 
+__global__ void gradient_array_mish_kernel(int n, float *activation_input, float *delta)
+{
+    int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if (i < n) {
+        float x = activation_input[i];
+        float d = 2 * expf(x) + expf(2 * x) + 2;
+        float w = 4 * (x + 1) + 4 * expf(2 * x) + expf(3 * x) + expf(x)*(4 * x + 6);
+        float derivative = expf(x) * w / (d * d);
+        delta[i] *= derivative;
+    }
+}
+
 __global__ void gradient_array_leaky_kernel(float *x, int n, float *delta)
 {
     int index = blockIdx.x*blockDim.x + threadIdx.x;
@@ -333,6 +355,13 @@ extern "C" void activate_array_swish_ongpu(float *x, int n, float *output_sigmoi
     CHECK_CUDA(cudaPeekAtLastError());
 }
 
+extern "C" void activate_array_mish_ongpu(float *x, int n, float *activation_input_gpu, float *output_gpu)
+{
+    const int num_blocks = get_number_of_blocks(n, BLOCK);
+    activate_array_mish_kernel << <cuda_gridsize(n), BLOCK, 0, get_cuda_stream() >> >(x, n, activation_input_gpu, output_gpu);
+    CHECK_CUDA(cudaPeekAtLastError());
+}
+
 extern "C" void gradient_array_ongpu(float *x, int n, ACTIVATION a, float *delta)
 {
     const int num_blocks = get_number_of_blocks(n, BLOCK);
@@ -354,4 +383,11 @@ extern "C" void gradient_array_swish_ongpu(float *x, int n, float *sigmoid_gpu,
     const int num_blocks = get_number_of_blocks(n, BLOCK);
     gradient_array_swish_kernel << <cuda_gridsize(n), BLOCK, 0, get_cuda_stream() >> > (x, n, sigmoid_gpu, delta);
     CHECK_CUDA(cudaPeekAtLastError());
+}
+
+extern "C" void gradient_array_mish_ongpu(int n, float *activation_input_gpu, float *delta)
+{
+    const int num_blocks = get_number_of_blocks(n, BLOCK);
+    gradient_array_mish_kernel << <cuda_gridsize(n), BLOCK, 0, get_cuda_stream() >> > (n, activation_input_gpu, delta);
+    CHECK_CUDA(cudaPeekAtLastError());
 }
\ No newline at end of file
diff --git a/src/activations.c b/src/activations.c
index c3ea4818aeb..5311fb97a77 100644
--- a/src/activations.c
+++ b/src/activations.c
@@ -46,6 +46,7 @@ ACTIVATION get_activation(char *s)
 {
     if (strcmp(s, "logistic")==0) return LOGISTIC;
     if (strcmp(s, "swish") == 0) return SWISH;
+    if (strcmp(s, "mish") == 0) return MISH;
     if (strcmp(s, "loggy")==0) return LOGGY;
     if (strcmp(s, "relu")==0) return RELU;
     if (strcmp(s, "elu")==0) return ELU;
@@ -133,6 +134,17 @@ void activate_array_swish(float *x, const int n, float * output_sigmoid, float *
     }
 }
 
+void activate_array_mish(float *x, const int n, float * activation_input, float * output)
+{
+    int i;
+    #pragma omp parallel for
+    for (i = 0; i < n; ++i) {
+        float x_val = x[i];
+        activation_input[i] = x_val;    // store value before activation
+        output[i] = x_val * tanh_activate(log(1 + expf(x_val)));
+    }
+}
+
 float gradient(float x, ACTIVATION a)
 {
     switch(a){
@@ -187,3 +199,16 @@ void gradient_array_swish(const float *x, const int n, const float * sigmoid, fl
         delta[i] *= swish + sigmoid[i]*(1 - swish);
     }
 }
+
+void gradient_array_mish(const int n, const float * activation_input, float * delta)
+{
+    int i;
+    #pragma omp parallel for
+    for (i = 0; i < n; ++i) {
+        float x = activation_input[i];
+        float d = 2 * expf(x) + expf(2 * x) + 2;
+        float w = 4 * (x + 1) + 4 * expf(2 * x) + expf(3 * x) + expf(x)*(4 * x + 6);
+        float derivative = expf(x) * w / (d * d);
+        delta[i] *= derivative;
+    }
+}
diff --git a/src/activations.h b/src/activations.h
index 19f3822c8e3..bba5ca8d10a 100644
--- a/src/activations.h
+++ b/src/activations.h
@@ -5,7 +5,7 @@
 #include "math.h"
 
 //typedef enum{
-//    LOGISTIC, RELU, RELIE, LINEAR, RAMP, TANH, PLSE, LEAKY, ELU, LOGGY, STAIR, HARDTAN, LHTAN, SELU
+//    LOGISTIC, RELU, RELIE, LINEAR, RAMP, TANH, PLSE, LEAKY, ELU, LOGGY, STAIR, HARDTAN, LHTAN, SELU, SWISH, MISH
 //}ACTIVATION;
 
 #ifdef __cplusplus
@@ -18,13 +18,17 @@ float activate(float x, ACTIVATION a);
 float gradient(float x, ACTIVATION a);
 void gradient_array(const float *x, const int n, const ACTIVATION a, float *delta);
 void gradient_array_swish(const float *x, const int n, const float * sigmoid, float * delta);
+void gradient_array_mish(const int n, const float * activation_input, float * delta);
 void activate_array(float *x, const int n, const ACTIVATION a);
 void activate_array_swish(float *x, const int n, float * output_sigmoid, float * output);
+void activate_array_mish(float *x, const int n, float * activation_input, float * output);
 #ifdef GPU
 void activate_array_ongpu(float *x, int n, ACTIVATION a);
 void activate_array_swish_ongpu(float *x, int n, float *output_sigmoid_gpu, float *output_gpu);
+void activate_array_mish_ongpu(float *x, int n, float *activation_input_gpu, float *output_gpu);
 void gradient_array_ongpu(float *x, int n, ACTIVATION a, float *delta);
 void gradient_array_swish_ongpu(float *x, int n, float *sigmoid_gpu, float *delta);
+void gradient_array_mish_ongpu(int n, float *activation_input_gpu, float *delta);
 #endif
 
 static inline float stair_activate(float x)
diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu
index 0b94dd29db4..a73f277ee92 100644
--- a/src/convolutional_kernels.cu
+++ b/src/convolutional_kernels.cu
@@ -392,7 +392,8 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
             */
 
             //add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h);
-            if (l.activation == SWISH) activate_array_swish_ongpu(l.output_gpu, l.outputs*l.batch, l.output_sigmoid_gpu, l.output_gpu);
+            if (l.activation == SWISH) activate_array_swish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.output_gpu);
+            else if (l.activation == MISH) activate_array_mish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.output_gpu);
             else if (l.activation != LINEAR && l.activation != LEAKY) activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation);
             //if(l.activation != LINEAR && l.activation != LEAKY) activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation);
             //if (l.binary || l.xnor) swap_binary(&l);
@@ -596,7 +597,8 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
 //#ifndef CUDNN_HALF
 //#endif // no CUDNN_HALF
 
-    if (l.activation == SWISH) activate_array_swish_ongpu(l.output_gpu, l.outputs*l.batch, l.output_sigmoid_gpu, l.output_gpu);
+    if (l.activation == SWISH) activate_array_swish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.output_gpu);
+    else if (l.activation == MISH) activate_array_mish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.output_gpu);
     else if (l.activation != LINEAR) activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation);
     //if(l.dot > 0) dot_error_gpu(l);
     if(l.binary || l.xnor) swap_binary(&l);
@@ -639,7 +641,8 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state
 
     if(state.net.try_fix_nan) constrain_ongpu(l.outputs*l.batch, 1, l.delta_gpu, 1);
 
-    if (l.activation == SWISH) gradient_array_swish_ongpu(l.output_gpu, l.outputs*l.batch, l.output_sigmoid_gpu, l.delta_gpu);
+    if (l.activation == SWISH) gradient_array_swish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.delta_gpu);
+    else if (l.activation == MISH) gradient_array_mish_ongpu(l.outputs*l.batch, l.activation_input_gpu, l.delta_gpu);
     else gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu);
 
     if (!l.batch_normalize)
diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c
index bf5beac7972..b76d7ee735f 100644
--- a/src/convolutional_layer.c
+++ b/src/convolutional_layer.c
@@ -473,10 +473,10 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w,
         l.scale_v = (float*)calloc(n, sizeof(float));
     }
 
-    if(l.activation == SWISH) l.output_sigmoid = (float*)calloc(total_batch*l.outputs, sizeof(float));
+    if (l.activation == SWISH || l.activation == MISH) l.activation_input = (float*)calloc(total_batch*l.outputs, sizeof(float));
 
 #ifdef GPU
-    if (l.activation == SWISH) l.output_sigmoid_gpu = cuda_make_array(l.output_sigmoid, total_batch*out_h*out_w*n);
+    if (l.activation == SWISH || l.activation == MISH) l.activation_input_gpu = cuda_make_array(l.activation_input, total_batch*out_h*out_w*n);
 
     l.forward_gpu = forward_convolutional_layer_gpu;
     l.backward_gpu = backward_convolutional_layer_gpu;
@@ -1100,7 +1100,8 @@ void forward_convolutional_layer(convolutional_layer l, network_state state)
                 add_bias(l.output, l.biases, l.batch, l.n, out_h*out_w);
 
                 //activate_array(l.output, m*n*l.batch, l.activation);
-                if (l.activation == SWISH) activate_array_swish(l.output, l.outputs*l.batch, l.output_sigmoid, l.output);
+                if (l.activation == SWISH) activate_array_swish(l.output, l.outputs*l.batch, l.activation_input, l.output);
+                else if (l.activation == MISH) activate_array_mish(l.output, l.outputs*l.batch, l.activation_input, l.output);
                 else activate_array_cpu_custom(l.output, m*n*l.batch, l.activation);
                 return;
 
@@ -1139,7 +1140,8 @@ void forward_convolutional_layer(convolutional_layer l, network_state state)
     add_bias(l.output, l.biases, l.batch, l.n, out_h*out_w);
 
     //activate_array(l.output, m*n*l.batch, l.activation);
-    if (l.activation == SWISH) activate_array_swish(l.output, l.outputs*l.batch, l.output_sigmoid, l.output);
+    if (l.activation == SWISH) activate_array_swish(l.output, l.outputs*l.batch, l.activation_input, l.output);
+    else if (l.activation == MISH) activate_array_mish(l.output, l.outputs*l.batch, l.activation_input, l.output);
     else activate_array_cpu_custom(l.output, l.outputs*l.batch, l.activation);
 
     if(l.binary || l.xnor) swap_binary(&l);
@@ -1276,7 +1278,8 @@ void backward_convolutional_layer(convolutional_layer l, network_state state)
     int n = l.size*l.size*l.c / l.groups;
     int k = l.out_w*l.out_h;
 
-    if (l.activation == SWISH) gradient_array_swish(l.output, l.outputs*l.batch, l.output_sigmoid, l.delta);
+    if (l.activation == SWISH) gradient_array_swish(l.output, l.outputs*l.batch, l.activation_input, l.delta);
+    else if (l.activation == MISH) gradient_array_mish(l.outputs*l.batch, l.activation_input, l.delta);
     else gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta);
 
     if (l.batch_normalize) {
diff --git a/src/layer.c b/src/layer.c
index e9ae67b5ff5..9fe4a439364 100644
--- a/src/layer.c
+++ b/src/layer.c
@@ -90,7 +90,7 @@ void free_layer(layer l)
 #endif  // GPU
     if (l.delta)              free(l.delta), l.delta = NULL;
     if (l.output)             free(l.output), l.output = NULL;
-    if (l.output_sigmoid)     free(l.output_sigmoid), l.output_sigmoid = NULL;
+    if (l.activation_input)   free(l.activation_input), l.activation_input = NULL;
     if (l.squared)            free(l.squared);
     if (l.norms)              free(l.norms);
     if (l.spatial_mean)       free(l.spatial_mean);
@@ -176,7 +176,7 @@ void free_layer(layer l)
     if (l.scale_updates_gpu)       cuda_free(l.scale_updates_gpu), l.scale_updates_gpu = NULL;
     if (l.input_antialiasing_gpu)  cuda_free(l.input_antialiasing_gpu), l.input_antialiasing_gpu = NULL;
     if (l.output_gpu)              cuda_free(l.output_gpu), l.output_gpu = NULL;
-    if (l.output_sigmoid_gpu)      cuda_free(l.output_sigmoid_gpu), l.output_sigmoid_gpu = NULL;
+    if (l.activation_input_gpu)    cuda_free(l.activation_input_gpu), l.activation_input_gpu = NULL;
     if (l.delta_gpu)               cuda_free(l.delta_gpu), l.delta_gpu = NULL;
     if (l.rand_gpu)                cuda_free(l.rand_gpu);
     if (l.squared_gpu)             cuda_free(l.squared_gpu);