Fix typos

HISTORY.md

@@ -6,7 +6,7 @@
 
 * ray distibutor (thanks @Vaishaal)
 * Remove tmp_dir only if the output dir is not in s3 (thanks @ezzarum)
-* support more intput formats (thanks @ldfandian)
+* support more input formats (thanks @ldfandian)
 
 ## 1.41.0
 
@@ -333,7 +333,7 @@ better aws s3 support:
 
 ## 1.5.3
 
-* increase stability by closing the pool and tarwriter explictely
+* increase stability by closing the pool and tarwriter explicitly
 
 ## 1.5.2
 

README.md

@@ -192,7 +192,7 @@ Img2dataset support several formats. There are trade off for which to choose:
 
 ## Encode format choice
 
-Images can be encoded in jpeg, png or webp, with diffent quality settings.
+Images can be encoded in jpeg, png or webp, with different quality settings.
 
 Here are a few comparisons of space used for 1M images at 256 x 256:
 

dataset_examples/laion-face.md

@@ -4,7 +4,7 @@
 
 LAION-Face is first used as the training set of [FaRL](https://github.com/FacePerceiver/FaRL), which provides powerful pre-training transformer backbones for face analysis tasks.
 
-For more details, please check the offical repo at https://github.com/FacePerceiver/LAION-Face .
+For more details, please check the official repo at https://github.com/FacePerceiver/LAION-Face .
 
 ## Download and convert metadata
 ```bash

examples/ray_example/README.md

@@ -7,7 +7,7 @@ If you are on AWS you can spin up a ray cluster this way:
 ``` ray up cluster_minimal.yaml ```
 
 Then you can run your job:
-```ray submit cluster_minmal.yaml ray_example.py -- --url_list <url_list> --out_folder <out_folder>```
+```ray submit cluster_minimal.yaml ray_example.py -- --url_list <url_list> --out_folder <out_folder>```
 
 You may also setup a ray cluster by following https://docs.ray.io/en/latest/cluster/getting-started.html
 

img2dataset/downloader.py

@@ -215,7 +215,7 @@ def data_generator():
                     _, sample_data = shard_to_dl[key]
                     str_key = compute_key(key, shard_id, oom_sample_per_shard, self.oom_shard_count)
                     meta = {
-                        # Skip columsn containing a the verification hash and only save the compute hash
+                        # Skip columns containing a the verification hash and only save the compute hash
                         **{
                             self.column_list[i]: sample_data[i]
                             for i in range(len(self.column_list))