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@@ -80,17 +80,17 @@ In practice, throughtput stays below the ideal curve when the number of gpus inc
 **Observation 2**: when batch_size is small (1, 2, 4, 8), throughput_amp ≈ throughput_tf32;  
 when batch_size is large (16, 32, 64, 128), throughput_amp > throughput_tf32.  
 
-<img src="https://gitlab.liu.se/xuagu37/Benchmark_nnU-Net_for_PyTorch/-/blob/main/figures/benchmark_throughput_batch_size.png" width="400">
+<img src="https://gitlab.liu.se/xuagu37/Benchmark_nnU-Net_for_PyTorch/-/raw/edb1b56ff5a9bb74bbd220c4fb717b6af6119232/figures/benchmark_throughput_batch_size.png" width="400">
 
 **Observation 3**: Benchmark results are more stable when larger batch_size.  
 
-<img src="https://gitlab.liu.se/xuagu37/Benchmark_nnU-Net_for_PyTorch/-/blob/main/figures/benchmark_throughput_cv.png" width="400">
+<img src="https://gitlab.liu.se/xuagu37/Benchmark_nnU-Net_for_PyTorch/-/raw/edb1b56ff5a9bb74bbd220c4fb717b6af6119232/figures/benchmark_throughput_cv.png" width="400">
 
 Coefficient of variation is calculated as the ratio of the standard deviation to the mean. It shows the extent of variability in relation to the mean of the population. 
 
 **Observation 4**: Ideally, the improvement of throughput would be linear when batch_size increases. In practice, throughtput stays below the ideal curve when batch_size > 16.
 
-<img src="https://gitlab.liu.se/xuagu37/Benchmark_nnU-Net_for_PyTorch/-/blob/main/figures/benchmark_throughput_batch_size_ideal.png" width="400">
+<img src="https://gitlab.liu.se/xuagu37/Benchmark_nnU-Net_for_PyTorch/-/raw/edb1b56ff5a9bb74bbd220c4fb717b6af6119232/figures/benchmark_throughput_batch_size_ideal.png" width="400">