Update validated TorchVision exports and compatibility documentation

Publish the reviewed model files and current CPU/GPU/NPU compatibility table.

README.md

@@ -42,6 +42,15 @@ acc@5 (on ImageNet-1K): 96.146%
 num_params: 28589128
     
 
+`convnext_tiny_int8_channelwise.tflite`: Mixed INT8/FP32 with channelwise INT8 weights. LayerNorm remains FP32.
+
+## Compatibility
+
+| File | CPU | GPU | NPU |
+|---|---|---|---|
+| `convnext_tiny.tflite` | Supported | Supported | N/A |
+| `convnext_tiny_int8_channelwise.tflite` | Supported | Not supported | Qualcomm / MediaTek |
+
 ## Intended uses & limitations
 
 The model files were converted from pretrained weights from PyTorch Vision. The models may have their own licenses or terms and conditions derived from PyTorch Vision and the dataset used for training. It is your responsibility to determine whether you have permission to use the models for your use case.
@@ -79,25 +88,25 @@ def preprocess(img: Image.Image) -> np.ndarray:
     w, h = img.size
     s = 236
     if w < h:
-        img = img.resize((s, int(round(h * s / w))), Image.BILINEAR)
+        img = img.resize((s, int(h * s / w)), Image.BILINEAR)
     else:
-        img = img.resize((int(round(w * s / h)), s), Image.BILINEAR)
-    left = (img.size[0] - 224) // 2
-    top = (img.size[1] - 224) // 2
+        img = img.resize((int(w * s / h), s), Image.BILINEAR)
+    left = int(round((img.size[0] - 224) / 2.0))
+    top = int(round((img.size[1] - 224) / 2.0))
     img = img.crop((left, top, left + 224, top + 224))
 
     x = np.asarray(img, dtype=np.float32) / 255.0
     x = (x - np.array([0.485, 0.456, 0.406], dtype=np.float32)) / np.array(
         [0.229, 0.224, 0.225], dtype=np.float32
     )
-    return np.expand_dims(x, axis=0)
+    return np.ascontiguousarray(x.transpose(2, 0, 1)[None])
 
 def main():
     ap = argparse.ArgumentParser()
     ap.add_argument("--image", required=True)
     args = ap.parse_args()
 
-    model_path = hf_hub_download("litert-community/convnext_tiny", “convnext_tiny.tflite")
+    model_path = hf_hub_download("litert-community/convnext_tiny", "convnext_tiny.tflite")
     labels_path = hf_hub_download(
         "huggingface/label-files", "imagenet-1k-id2label.json", repo_type="dataset"
     )

convnext_tiny.tflite

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:e539be30ec474cd81a98d7789016fd7e1d30b9724fbb6f45ba28a6f55bf1126e
-size 114371008
+oid sha256:42f4181cae412cd35fc769d4baa25d13d5e38a399fe37b0f8872b5b849571188
+size 114425184

convnext_tiny_int8_channelwise.tflite

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:95e9aac495675b7e73cc91a7dfcb3b8474b8c06ae53e0815ee290c6cad9a25c7
+size 29861536