Update README.md

README.md

@@ -1,72 +1,73 @@
----
-license: apache-2.0
-tags:
-- jax
-- safetensors
----
-
-# Baseline PerceptNet
-
-## Model Description
-
-## How to use it
-
-### Install the model's package from source:
-```
-git clone https://github.com/Jorgvt/paramperceptnet.git
-cd paramperceptnet
-pip install -e .
-```
-
-### 1.Import necessary libraries:
-
-```
-import json
-
-from huggingface_hub import hf_hub_download
-import flax
-import orbax.checkpoint
-from ml_collections import ConfigDict
-
-from paramperceptnet.models import Baseline as PerceptNet
-```
-
-### 2.Download the configuration
-
-```
-config_path = hf_hub_download(repo_id="Jorgvt/ppnet-baseline",
-                              filename="config.json")
-with open(config_path, "r") as f:
-    config = ConfigDict(json.load(f))
-```
-
-### 3. Download the weights
-
-#### 3.1. Using `safetensors`
-
-```
-from safetensors.flax import load_file
-
-weights_path = hf_hub_download(repo_id="Jorgvt/ppnet-baseline",
-                               filename="weights.safetensors")
-variables = load_file(weights_path)
-variables = flax.traverse_util.unflatten_dict(variables, sep=".")
-params = variables["params"]
-```
-
-#### 3.2. Using `mgspack`
-```
-weights_path = hf_hub_download(repo_id="Jorgvt/ppnet-fully-trained",
-                               filename="weights.msgpack")
-with open(weights_path, "rb") as f:
-    variables = orbax.checkpoint.msgpack_utils.msgpack_restore(f.read())
-variables = jax.tree_util.tree_map(lambda x: jnp.array(x), variables)
-params = variables["params"]
-```
-
-### 4. Use the model
-
-```
-from jax import numpy as jnp
-pred = model.apply({"params": params}, jnp.ones((1,384,512,3)))
-```
+---
+license: apache-2.0
+tags:
+- jax
+- safetensors
+---
+
+# Baseline PerceptNet
+
+## Model Description
+
+## How to use it
+
+### Install the model's package from source:
+```
+git clone https://github.com/Jorgvt/paramperceptnet.git
+cd paramperceptnet
+pip install -e .
+```
+
+### 1.Import necessary libraries:
+
+```
+import json
+
+from huggingface_hub import hf_hub_download
+import flax
+import orbax.checkpoint
+from ml_collections import ConfigDict
+
+from paramperceptnet.models import Baseline as PerceptNet
+```
+
+### 2.Download the configuration
+
+```
+config_path = hf_hub_download(repo_id="Jorgvt/ppnet-baseline",
+                              filename="config.json")
+with open(config_path, "r") as f:
+    config = ConfigDict(json.load(f))
+```
+
+### 3. Download the weights
+
+#### 3.1. Using `safetensors`
+
+```
+from safetensors.flax import load_file
+
+weights_path = hf_hub_download(repo_id="Jorgvt/ppnet-baseline",
+                               filename="weights.safetensors")
+variables = load_file(weights_path)
+variables = flax.traverse_util.unflatten_dict(variables, sep=".")
+params = variables["params"]
+```
+
+#### 3.2. Using `mgspack`
+```
+weights_path = hf_hub_download(repo_id="Jorgvt/ppnet-fully-trained",
+                               filename="weights.msgpack")
+with open(weights_path, "rb") as f:
+    variables = orbax.checkpoint.msgpack_utils.msgpack_restore(f.read())
+variables = jax.tree_util.tree_map(lambda x: jnp.array(x), variables)
+params = variables["params"]
+```
+
+### 4. Use the model
+
+```
+from jax import numpy as jnp
+model = PerceptNet(config)
+pred = model.apply({"params": params}, jnp.ones((1,384,512,3)))
+```